A FRAMEWORK FOR APPLICATION SPECIFIC KNOWLEDGE ENGINES by Guanpi Lai

A FRAMEWORK FOR APPLICATION SPECIFIC KNOWLEDGE ENGINES by Guanpi Lai
A FRAMEWORK FOR APPLICATION SPECIFIC KNOWLEDGE ENGINES
by
Guanpi Lai
_____________________
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF SYSTEMS AND INDUSTRIAL ENGINEERING
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2010
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by Guanpi Lai
entitled A Framework for Application Specific Knowledge Engines
and recommend that it be accepted as fulfilling the dissertation requirement for the
Degree of Doctor of Philosophy
_______________________________________________________________________
Date: 4/28/2010
Fei-Yue Wang
_______________________________________________________________________
Date: 4/28/2010
Ferenc Szidarovszky
_______________________________________________________________________
Date: 4/28/2010
Jian Liu
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
________________________________________________ Date: 4/28/2010
Dissertation Director: Fei-Yue Wang
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advanced degree at the University of Arizona and is deposited in the University Library
to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission, provided
that accurate acknowledgment of source is made. Requests for permission for extended
quotation from or reproduction of this manuscript in whole or in part may be granted by
the head of the major department or the Dean of the Graduate College when in his or her
judgment the proposed use of the material is in the interests of scholarship. In all other
instances, however, permission must be obtained from the author.
SIGNED: Guanpi Lai
4
ACKNOWLEDGEMENTS
I wish to thank my committee members who were more than generous with their
expertise and precious time. A special thanks to Prof Fei-Yue Wang, my dissertation
advisor and committee chair for his countless hours of reflecting, reading, encouraging,
and most of all patience throughout the entire process. Thank you Prof. Ferenc
Szidarovszky, Dr. Daniel Zeng, and Dr. Jian Liu for agreeing to serve on my committee.
I especially thank Yanqing Gao, Yilu Zhou, Jialun Qin and many others for their
encouragement and emotional support during my tough time.
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DEDICATION
This dissertation is dedicated to my family: my wife Xuetao Xu, my child Lucas Luming
Lai, my parents Yangfu Lai and Chunrong Yang, and my parents-in-law Furong Xu and
Guiju Liang. I give my deepest expression of love and appreciation for the
encouragement that you gave during this long journey.
6
TABLE OF CONTENTS
LIST OF TABLES ....................................................................................... 9
LIST OF FIGURES ................................................................................... 10
ABSTRACT .............................................................................................. 11
CHAPTER 1
INTRODUCTION ............................................................ 13
1.1
Motivation and Research Description ............................................................ 14
1.2
Organization of the Dissertation ..................................................................... 17
CHAPTER 2
UNDERSTAND DATA ON THE WEB........................... 20
2.1
Two worlds of Data – Unstructured and Structured ....................................... 20
2.1.1 Manage unstructured data ....................................................................... 21
2.1.2 Structured data on the Web ..................................................................... 28
2.2
Life on the Web .............................................................................................. 33
2.2.1 Online Communities ............................................................................... 33
2.2.2 Peer-to-Peer World .................................................................................. 41
2.3
Conclusions .................................................................................................... 46
CHAPTER 3 A FRAMEWORK FOR APPLICATION SPECIFIC
KNOWLEDGE ENGINES ........................................................................ 47
3.1
Knowledge Portals and Applications ............................................................. 49
3.2
An Overview of the Framework for Application Specific Knowledge
Engines… .................................................................................................................... 54
3.3
Construction of Data Repositories.................................................................. 55
3.3.1 Data Collection ........................................................................................ 55
3.3.2 Data Preparation ...................................................................................... 71
3.3.3 Data Silo .................................................................................................. 72
3.4
Searching by KCF with Result Presentation .................................................. 79
3.4.1 KCF Processing ....................................................................................... 79
3.4.2 Semantic Search ...................................................................................... 83
3.4.3 Result Presentation .................................................................................. 85
3.5
Conclusions .................................................................................................... 89
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TABLE OF CONTENTS - Continued
CHAPTER 4 SEARCHING TERRORIST GROUPS ON THE
INTERNET…… ........................................................................................ 90
4.1
Literature Review ........................................................................................... 92
4.1.1 Digital Archiving for Terrorists’ Resources............................................ 94
4.1.2 Terrorism Research Portals ..................................................................... 95
4.1.3 Multilingual Issues .................................................................................. 97
4.2
Research Questions......................................................................................... 98
4.3
Implementation of Dark Web Portal .............................................................. 99
4.3.1 Dark Web Data Collection Building ....................................................... 99
4.3.2 Post-retrieval Analysis and Multilingual Support ................................. 115
4.3.3 Searching and Browsing in the Dark Web Portal ................................. 119
4.3.4 Multilingual Support ............................................................................. 124
4.3.5 Semantic Search in the Dark Web......................................................... 126
4.4
Conclusions and Future Directions............................................................... 130
CHAPTER 5
MONITOR FILE SHARING IN P2P WORLD ...............132
5.1
Literature Review ......................................................................................... 133
5.1.1 P2P History ........................................................................................... 133
5.1.2 P2P Networks ........................................................................................ 136
5.1.3 Related P2P Research............................................................................ 141
5.2
Implementation of Building ASKE Data Collection .................................... 143
5.2.1 Resource Identifier ................................................................................ 143
5.2.2 Spider agents ......................................................................................... 145
5.2.3 Content Filter......................................................................................... 149
5.3
Services and Case Study ............................................................................... 152
5.3.1 Services for Copyright Owners ............................................................. 152
5.3.2 Case Study – Watchmen ....................................................................... 157
5.4
Conclusions .................................................................................................. 161
CHAPTER 6
CONCLUSIONS AND FUTURE DIRECTIONS ............162
6.1
Conclusions .................................................................................................. 162
6.2
Future Directions .......................................................................................... 163
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TABLE OF CONTENTS - Continued
APPENDIX A US DOMESTIC EXTREMIST GROUPS AND URLS ....165
APPENDIX B INTERNATIONAL TERRORIST GROUPS AND URLS
FOR ARABIC GROUPS ..........................................................................178
APPENDIX C US DOMESTIC EXTREMIST FORUMS ........................202
APPENDIX D PART OF SOURCE CODES OF EDONKEY SPIDER
AGENTS ..................................................................................................212
APPENDIX E PART OF SOURCE CODES OF BITTORRENT SPIDER
AGENTS ..................................................................................................223
APPENDIX F SOURCES FOR WATCHMEN IN EDONKEY
NETWORKS AND BITTORRENT NETWORKS ...................................235
REFERENCES .........................................................................................265
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LIST OF TABLES
Table 2.1 Profile of the types of analytic users in a typical organization ......................... 22
Table 3.1 Popular Forum Software Packages ................................................................... 66
Table 3.2 Different Types of Download Servers .............................................................. 67
Table 4.1 Current Approaches to Archive Terrorists’ Web Resources ............................ 93
Table 4.2 Number of pages collected for the terrorist groups within each category ...... 107
Table 4.3 Documents spidered in the second batch for US Domestic Extremist Groups
......................................................................................................................................... 108
Table 4.4 Documents spidered in the second batch for Arabic-Speaking Terrorist Groups
......................................................................................................................................... 109
Table 4.5 Documents spidered in the second batch for Spanish-Speaking Terrorist Groups
......................................................................................................................................... 110
Table 4.6 Comparison between the 1st and 2nd batch collection ................................... 110
Table 4.7 Summary of Document Types in the Forum Collection ................................. 112
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LIST OF FIGURES
Figure 2.1 The screenshot showing the main forum discussing page............................... 34
Figure 2.2 The screenshot showing the threads in a specific forum discussion ............... 35
Figure 2.3 How web users participate in online community? .......................................... 36
Figure 2.4 Part of torrent file list for “Avatar” on the thepiratebay.org ........................... 42
Figure 2.5 The screenshot downloading “Avatar.3D” using BitTorrent .......................... 43
Figure 3.1 The framework and components of an Application Specific Knowledge
Engine ............................................................................................................................... 56
Figure 3.2 The structure of Resource Identifer ................................................................. 58
Figure 3.3 The architecture of social sensor networks ..................................................... 61
Figure 3.4 Ontology-development process ....................................................................... 75
Figure 3.5 The architecture of Metadata Extractor ........................................................... 76
Figure 4.1 Number of U.S. domestic groups identified from each source within each
category ........................................................................................................................... 104
Figure 4.2 Number of international groups identified from each source within each
geographical location ...................................................................................................... 104
Figure 4.3 The social sensor network for U.S. Domestic Extremist Groups .................. 106
Figure 4.4 Summary and categorization of identified U.S. domestic extremist forums . 111
Figure 4.5 An example of U.S. domestic extremist forums............................................ 114
Figure 4.6 The distributions of number of participants and number of postings on
extremist Forums ............................................................................................................ 115
Figure 4.7 The screenshots of Dark Web Portal for U.S. domestic groups .................... 127
Figure 4.8 The screenshots of Dark Web Portal for Arabic-speaking terrorist groups .. 128
Figure 4.9 Jihad multilingual portal user sessions .......................................................... 129
Figure 4.10 The screenshot of semantic search for the keywords "Roubaix Gang"....... 130
Figure 5.1 The Architecture of P2P Spider Agents ........................................................ 147
Figure 5.2 Customize user profile ................................................................................... 154
Figure 5.3 Infringement overview page .......................................................................... 155
Figure 5.4 Infringement search interface ........................................................................ 155
Figure 5.5 Top 5 countries of peers and trackers for “Quantum of Solace” between
11/03/2008 to 11/21/2008 ............................................................................................... 156
Figure 5.6 Protocols breakdown ..................................................................................... 158
Figure 5.7 Daily infringements trend – all infringements............................................... 160
Figure 5.8 Top 10 ISPs ................................................................................................... 160
Figure 5.9 Top 10 filenames ........................................................................................... 161
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ABSTRACT
The amount of information on the Internet has been proliferated rapidly in recent years as
new technologies and applications become popular. The broad heterogeneous contents
bring us a substantial challenge in the field of knowledge discovery and information
retrieval. The objective of this dissertation is to design and implement a systematic
framework to help users access huge and various information on the Web by combining
different techniques and algorithms in different domains. In this dissertation, we propose
an effective Application Specific Knowledge Engine framework to build structured and
semantic data repositories, and support keyword search and semantic search. The
framework is consistent with the architecture of most search engines. It enhances the
general search engines in three ways: various data retrieval ability; semantic data support;
and post-retrieval analysis. Various techniques and algorithms that could facilitate
knowledge discovery are used in the framework.
In the first part, we review different types of data on the Internet and approaches to
retrieve various data: structured and unstructured data, online community data, and Peerto-Peer data. After that we present an overview of the system architecture of the ASKE
framework, and especially discuss the core components of the framework in details.
The following chapters aim to investigate how the ASKE framework can be applied in
two different domains (counter-terrorism and anti-piracy). We present the research in
developing a counter-terrorism knowledge portal that incorporates various data collection
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and post-retrieval analysis. The process of building the portal following ASKE
framework is described. The details of the data collections of Web sites and online
forums are also reported. In the anti-piracy domain, we mainly discuss building Peer-toPeer data collection and serving users with customized profiles. A case study of
monitoring the movie Watchmen piracy on typical Peer-to-Peer Networks is discussed
also.
This dissertation has two main contributions. Firstly, it demonstrates how information
retrieval, Web mining and other artificial intelligence techniques can be used in
heterogeneous environment. Secondly, it provides a feasible framework which can
facilitate users to discover knowledge in their specific searching and browsing activities.
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CHAPTER 1
INTRODUCTION
In this dissertation, we demonstrate that effective and efficient knowledge discovery has
become a more and more challenging problem, as the massive amount of Web pages and
data being added to the Web every day. People have published a large number of
academic papers on knowledge discovery with the continually increased interest, which
can be attributed to information overload and data explosion, the advances in information
technologies, and the need for organizations and individuals who are expected to keep
their competence, although most of them target to solve a specific part of the problem.
We propose a highly integrated framework, called Application Specific Knowledge
Engine [1] (ASKE), for intelligent interactive information retrieval and knowledge
discovery from the Internet, and exploit different kinds of existing technologies such as
data mining, text mining, and web mining, also include the related research topics with
Web spidering and general search engines. We also illustrate how this framework is
applied to different applications (Dark Web [2] and Peer-to-Peer World).
A 1998 report [3] on database research foretold that the Internet will hold the majority of
published human knowledge. With the advanced Web technologies (e.g. Web 2.0), never
has it been easier than with the Internet to publish all kinds of digital materials and make
them instantly available to anyone. However, as the amount of data grows exponentially,
availability extends to the issue of universal accessibility. It becomes more and more
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necessary for us to seek alternative methods to effectively and efficiently discover and
use the invaluable assets hidden in huge amount of data.
As text search on search engines become routine as millions of users use them daily to
pinpoint resources on the Internet, knowledge discovery capability on the Internet is still
limited and sometimes even absent when simple keyword searches can bring back
hundreds of thousands of document results. The most popular keyword indexing
techniques in most of the current information retrieval systems available on the web
emphasize very little on context or textual information, which is actually very important
during the knowledge discovery process. It’s imperative to break the limitation of simple
keyword searches and take account into implicit semantic information. In addition, search
engines lack the ability to unveil the details of people’s activities on the Internet (e.g.
downloading movies/music using different applications and protocols, chatting in open
chat rooms, posting posts on forums, etc.)
1.1 Motivation and Research Description
We motivate the dissertation by discussing a number of open problems in the previous
section.
1. Information Overload in the World Wide Web space
"Information overload" is a term referring to an excess amount of information being
provided, making processing and absorbing tasks very difficult for the individual because
sometimes we cannot see the validity behind the information [4], created by Alvin Toffler.
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In the World Wide Web (WWW) space, more and more people are not only the
consumers of data but also producers. As more and more new technologies are developed,
it becomes much easier to retrieve, produce and deliver information than in earlier period,
and thus all relevant or irrelevant data is published instead of only the most important one.
This results the explosion in more and more unclear and inaccurate information on the
Web. Finding real useful information is often a hit-and-miss process.
Using searching engines to find data in WWW space is a well-known approach, however,
almost everyone would agree that extracting information from Web via the search engine
technology is still a skill that not many people are able to master. Widely used search
engines tend to return thousands of answers for even very specific queries, which do not
have any kind of underlying semantic structure that could facilitate knowledge discovery.
2. Diversity of Information Sources
It becomes a big problem that valuable information reside in diverse sources, as today we
lack the mature methods to locate, access and integrate various information. As people
and organizations rely more and more on the Web for their daily lives and daily
operations, the data on the Web evolves into huge diversity in both locations and formats.
There are two issues are involved. The first one is how to collect diverse information
from sparse locations efficiently, and second is how to store the data and integrate them.
It usually creates large, rigid, and practically unmanageable systems by tightly integrating
diverse sources into a single system to centralize the information sources.
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Using federated systems [5] may be an alternative. However, a central data management
dictionary is required to translate search queries into terms/words that the diverse
information sources can understand in most federated systems. The federated systems
have to update the dictionary frequently, thus make the entire data sources unusable and
unavailable.
3. Data Mining and Text Mining
A lot of data is stored today so that people can discover valuable knowledge and use them
during the decision making process. Data and text mining tools are required to extract
useful information from large amounts of diverse information sources.
Generally, data mining, also called Knowledge Discovery in Database (KDD), is the
process of analyzing data from different perspectives and summarizing it into valid, novel,
potentially useful, and ultimately understandable information - information that can be
used in decision making from different types of data storages, such as databases, data
silos, etc. It allows users to analyze data from many different dimensions or angles,
categorize it, and summarize the relationships identified. Technically, data mining is the
process of finding correlations or patterns among dozens of fields in the data that are
organized in records structured by categorical, ordinal and continuous variables, such as
large relational databases.
However, most of data that are stored today are actually unstructured. Up to 90% of all
organizational data is stored in some sort of unstructured text [6] according to a recent
study by Merrill Lynch and Gartner,. Text mining is the process of deriving novel
17
information, such as relations, hypotheses or trends that are hidden in a collection of
unstructured text files. It numerates and characterizes the unstructured documents into
structured table representation which can be explored by data mining tools. How to
utilize and integrate data mining and text mining technologies to discover knowledge
from a high volume of unstructured data from the Internet becomes a challenging issue.
4. Data Repository
The Internet is a collection of objects with complex structures. These objects can be
physical, like web pages, documents, images, videos, sound, maps, games, applications,
data files etc. They can be also virtual, like users, hosts, network etc., as some roles on
the Web. These objects are distributed and stored, or represented, on a large set of
heterogeneous repositories. It’s very costly to query such data due to the huge semantic
ambiguities and the heterogeneity of the data sources.
One way to reduce the cost is by storing the processed data in a relational table at a more
general conceptual level[7]. Users may scan the general description of the information on
the Internet by applying high level queries directly on the processed data. Another way is
to develop a semantic-web-based data repository by using the Semantic Web technology,
which provides consistent formats to integrate and combine of data which is from diverse
sources and language for representing the relationships between the data and real world
objects.
1.2 Organization of the Dissertation
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Traditional search techniques cannot satisfy people’s needs for knowledge discovery
from heterogeneous data on the Internet. To address this issue, we propose the following
research questions:
1. How can we develop a generic framework to facilitating searching and browsing
on the Internet by integrating various data collection techniques and post-retrieval
analysis from heterogeneous data?
2. How can we build structured data repositories and semantic data repositories
systematically and efficiently?
3. How can we apply the generic framework to different applications and domains?
In Chapter 2, we present the research on different types of data on the Internet, and
approaches to retrieve heterogeneous data: structured and unstructured data, online
community data, and P2P data.
Chapter 3 demonstrates the concepts of ASKE and proposes a feasible approach in many
other different applications. We present an overview of the system architecture of the
ASKE framework, and explain the core components of the framework.
In Chapter 4, we present the research in developing a counter-terrorism knowledge portal
that incorporates various data collection techniques and post-retrieval analysis. The
process of building the portal following ASKE framework is described. We also report
the data collections of Web sites and online forums.
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In Chapter 5, we mainly discuss how to monitor P2P networks using ASKE framework.
Different P2P protocols and current research topics in P2P networks are reviewed. The
detail of implementation of building data collection is presented. We also report the AntiPiracy system which uses the P2P data collection to serve users with customized profiles.
A case study for monitoring the movie Watchmen piracy on eDonkey network and
BitTorrent Network is discussed.
Chapter 6 summarizes conclusions of this dissertation and suggests future directions.
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CHAPTER 2
UNDERSTAND DATA ON THE WEB
Today’s Web has millions terabytes of information available to humans, but not
accessible to computers. Most of the data on the web reside in HTML pages, which are
formatted in esoteric ways that are difficult for machines to process and to locate for
humans. In addition, the Web is not only comprised of millions of web pages but also
people’s actives as Web 2.0 techniques and social network become more and more
popular. To discover knowledge from the huge data, it would be necessary to understand
the data in the ways of structures, formats, accessibilities, and activities.
In this chapter, we explore the characteristics of data on the Web, and review the ways to
approach the problems that are the results of these features.
2.1
Two worlds of Data – Unstructured and Structured
Since the Web emerged, unstructured content has dominated the Web. The techniques of
Information Retrieval are applied broadly in searching the Web. Particularly, it's a truism
that more than 85 percent of business-relevant information exists as unstructured content,
such as e-mails, reports, letters, surveys, white papers, memos, news, user groups, chats,
marketing material, presentations and Web pages. This figure (85 percent) is very widely
cited by analysts and vendors [8].
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People use unstructured data on the Web every day, creating, storing, delivering and
retrieving web pages, reports, e-mails, spreadsheets and other types of documents.
Unstructured data consists of any data stored in an unstructured format at an atomic level.
There is no conceptual definition and no data type definition in the unstructured content.
In textual documents, a word is simply a word. Most of unstructured data are textual
objects, which are based on pre-defined platforms or media, such HTML, Microsoft
Word documents, e-mails, presentation slides or spreadsheets.
In the mean time, people also use structured data a lot. Structured data usually has an
enforced form composed of different atomic data types. Structured data supports
querying and reporting against predetermined data types and understood relationships, as
structured data is organized in a highly regular way, such as in tables and relations, where
the regularities apply to all the data in a particular dataset [9].
2.1.1
Manage unstructured data
To manage unstructured data efficiently is considered as one of the major challenged
problems in the information technology research areas. White-collar workers spend
anywhere from 30 to 40 percent of their time managing documents, up from 20 percent of
their time according to investigations from Gartner. The main reason is that the
techniques which can successful transform structured data into business intelligence
simply don't work with unstructured data, as they relies much on the structure of data.
Compared with structured data, three distinct challenges are raised for unstructured data:
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1
Although a lot of unstructured data does formatted in some forms, such as
HTML, XML or Microsoft Word templates, the data is still not accessible from
a semantic level.
2
We cannot gain insight from unstructured data as the context of the information
is missing.
3
And lastly, the semantic meaning of information is interpreted largely
subjectively instead of objectively.
To understand the extension of this problem, Giga Research conducted a study to profile
the types of analytic users in a typical organization. In Table 2.1, the rightmost column
shows that all classes of users spend considerable amounts of time using decision support
tools and applications. From this table, we can notice that the vast majority of decision
makers in an organization are information consumers, and typical business intelligence
solutions are generated based on structured data alone thus satisfy decision makers
requirements.
Type of Users
% of All
End Users
% That are
Producers
Data/Analysis
% That are
Consumers of
Information
Decision
Makers
IT
Power User
Business User
Casual User
Extended
Enterprise User
2
5
25
30
38
98
84
18
8
3
2
16
82
92
97
No
No
Yes
Yes
Yes
Table 2.1 Profile of the types of analytic users in a typical organization
% of Work
Time
Allocated to
Business
Intelligence
Solutions
15
42
12
4
2
23
The common approach to manage unstructured data is to convert them into structured
data so that all the efficient tools and techniques which are useful for structured data can
be directly applied. Search engines play very important roles during this process.
1. Identify and Locate Data - Search
The first step to manage unstructured documents is to make them searchable. Prior to the
emergence of the Web, full-text and other text-search techniques were mainly applied
within library, document-management and database management systems. However, with
the exponential growth of the Internet, searching on the Web quickly became the main
way to retrieve information. According to market-research firm Outsell Inc., office
workers now spend an average of 9.5 hours each week searching, gathering and
analyzing information; and nearly 60 percent of that time, or 5.5 hours a week, is spent
on the Internet, at an average cost of $13,182 per worker per year [10].
All this searching is not efficient as we expected. Current Web search engines are
implemented similarly to traditional information-retrieval systems: indexes of keywords
within documents are generated and stored in database systems, and when a user query is
received, the query is split into several keywords which are searched in the database
system, finally a ranked list of documents is returned. The quality for search results relies
much on the input user queries which are out of control of search engines. In fact, the
average length of search terms used on the public Web is only 1.5 to 2.5 words, as several
studies show. In addition, the average search contains efficient Boolean operators (such
as and, or and not) is fewer than 10 percent of the time. With such short and ambiguous
24
queries and lack of advanced search techniques, the results are not good enough. A
performance assessment of the top five Web search engines, conducted by the U.S.
National Institute of Standards and Technology, showed that when 2.5 search words are
used, only 23 to 30 percent of the first 20 documents returned are actually relevant to the
query.
The search-engine vendors have continued to improve their technology with the
acknowledgement of the weakness of basic keyword search. For example, Google has
added techniques such as stemming and spelling correction, time and category
classification to its widely used search engine, while Powerset employs natural language
processing [11].
2. Search in Context
Another problem of Web search engines is that they don’t take account of the context of
data, as they treat each search request independently. Although users’ interests differ, if
they input same queries, the results will be identical for every user. For example, for the
query “smart phones", the first page of results for a recent query to Google is the
Wikipedia page about “smart phones”, introducing the history, operating systems etc.
about “smart phones”. This is useful information if the user is interested in the idea of
what smart phones are, but not for a buyer interested in shopping a smart phone. If the
context for the query is known to be that the user is buying a smart phone, it is possible to
refine the query accordingly via knowing the type of result in the first place.
25
Adding contextual information would help improve search efficiency a lot. Contextual
information generally is meta data, and it helps narrow down the range of possible results.
It can be also extracted from carefully-crafted set of indexed resources. "Parametric
selection" allows users to locate and access data by providing a way to utilize available
meta data or context description [12] by filtering and sorting on known meta data fields
such as geographic locations, time, product codes, topics etc. By carefully selecting only
the relevant fields, the user can tremendously reduce the number of results that may be
more relevant to the search.
To construct an "advanced search" form is another popular approach. Users can select
options on meta data to specify their search in more details. For example, Atomz's
Content Mining Engine believes that meta data-assisted search overcomes many of the
limitations of standard keyword search [13].
3. Classification and Taxonomy
Structured data is usually organized as rows and columns, such as in spreadsheets or
relational databases. Similarly, we can manage unstructured data in systems with a
hierarchical structure, which is called “taxonomy”. A taxonomy provides users a
convenient, intuitive way to explore and access information, similar to the file directories
in Microsoft Windows systems. The users can drill down through the categories and
subcategories of the taxonomy until they find the relevant data or documents, rather than
formulating a query and then go through the ranked results. In addition, the taxonomy can
26
be used as meta data in advanced search forms to limit queries by specifying categories
and sub-categories.
The process of organizing or converting unstructured data into a taxonomy is referred to
as classification, which is a widely used technique in various fields, including data
mining [14]. It classifies a large set of objects into predefined classes, described by a set
of attributes, using different types of supervised learning methods. Classification learning
algorithms have been extensively implemented in machine learning with quite a long
history. Pure symbolic machine learning algorithms are the most common ones, although
some of them are specialized in particular types of data and domains, which are very
useful in specific domain knowledge retrieval. There are many different types for
classification algorithms: a) rule based algorithms, CN2 [17] and CL2 [18]; b) decision
tree algorithms, ID3 [15] and C4.5 [16] etc.; c) pure statistical algorithms, CART [19],
genetic algorithms [20], adaptive spline methods [21] and graphical models [22]; d)
Nonlinear algorithms based on neural networks, back-propagation networks [23], and
nonlinear regression; e) example-based algorithms, PEBLS [24], algorithms based on
inductive logic programming(e.g. [25, 26]) and hybrid systems (e.g. [27]); f) others,
Support vector machines [28, 29], conformal predictors [30] and Bayesian classifiers [31],
etc.
Although many people agree taxonomy/classification could be the standard way of
processing unstructured data, there are couple stumbling challenges. One major issue is
the unacceptably low accuracy results of automated classification systems. These systems
27
usually only use a single technique, such as a rule-based or a example-based approach as
we mentioned above, which works fine in some data or domains, but not in others. For a
particular data set, we have to test different algorithms, select the best fit, and tune it up.
The difficulty of maintaining taxonomies would be another challenging issue. This task
requires individuals who both understand domain knowledge and happen to have rich IT
experience. This kind of human resources is hardly to find and would cost a lot. In
addition, a high quality taxonomy with broad coverage may be at least eight to 10 levels
deep and contain hundreds, even thousands, of categories. Since these taxonomies are
based on dynamic data, such as techniques, products, company organizational structures,
or financial data etc., they need to be modified very frequently. Updating and maintaining
such taxonomies are not an easy job, both time-consuming and expensive.
4. Content Intelligence: Toward a Solution
Search engines are very helpful to locate unstructured web pages via keywords or simple
logic combinations of keywords. The common form that search results are presented is a
list, and each search result (URL/link) is independent. A new enhanced search and
intelligent classification system are expected. This system will provide intelligent
services that generate incremental value for users.
The solution “content intelligence” would be fully developed applications that are not
limited to search and document classification. These applications could help users
navigate and access their unstructured data, extract meta data from documents, classify
the documents, build up a taxonomy, and provide a sophisticated user interface for
28
browsing the documents’ hierarchy. In addition, through system interfaces, such as APIs
or XML-based Web services, applications can be isolated from the underlying shaggy
information hierarchy, which may change very often.
Content intelligence is supported by the following technologies: search, classification and
discovery. Discovery here describes the process of automatically searching large volumes
of data for patterns that can be considered as the knowledge about the data. Content
intelligence will be used to uncover new issues and trends and to answer specific business
questions, akin to business intelligence, thus unstructured data on the Web will become a
source of
a source of valuable, accessible, time-critical knowledge repository and
business intelligence.
2.1.2
Structured data on the Web
Although the Web is dominated by unstructured data for a long time, we can see a huge
increase both in the amount of structured data on the Web and in the diversity of the
structures in which these data are stored. Most recent examples of structure data are
various annotation schemes (e.g., Digg [32], Flickr [33], Twitter [34]) that allow users to
add labels/tags to content, like pages or images. A majority of structure data can be found
in the deep Web (also called the invisible Web or the hidden Web), which is referring to
dynamic contents or pages on the Web that are stored in databases and served by a
submitted query or accessed only through a HTML form. In the following section, we
describe these two kinds of structured data that exists on the Web today, and discuss the
level of structure for each kind of data.
29
1. The Deep Web
The deep Web contains the Web’s information that is buried far down on dynamically
generated sites, and standard search engines do not find. Traditional search engines
cannot retrieve content in the deep Web until those pages are created dynamically as the
result of a specific query request is sent to the website. This content is considered
invisible because web crawlers rely on hyperlinks to discover new content, and crawlers
do not have the ability to fill out arbitrary HTML forms or generate dynamic queries.
Even web crawlers can get dynamic content, the result data doesn’t contain the structure
nature which is built in the backend databases.
No one knows the size of the deep Web for sure. In 2000, Michael Bergman estimated
that the deep Web contained approximately 7,500 terabytes of data and 550 billion
individual documents [35]. Danny Sullivan, a search engine expert and formerly of
Search Engine Watch, wrote in 2000 that the invisible Web was about 500 times Google's
index of one billion pages [36]. New estimates of Google's index sets it at over 8 billion
in 2005 [37], and search engines are said to only crawl 16-20% of the Internet [38]. The
content on the deep Web is believed to be possibly much more than the current indexed
WWW, and typically is of very high-quality [35].
To understand how much deep web content exists, there is no way to just count the
number of forms on the Web. For instance, there are numerous applications and forms on
different types of web sites, but actually utilize the same underlying data source, e.g.
databases. As most of major search engines provide codes to put their search forms on
30
any web sites, a large number of forms on the web are simply the search boxes from
search engines. Online communities and Web 2.0 applications are another two large
sources of forms. People contribute user specific data, like posts, comments, blogs to
these web sites. In addition, forms are also used for applications such as logins,
subscriptions, comments, etc. Looking into these different types of forms, we can find
that none of which present useful structured data to end users directly.
The content on the deep web cover a wide range of different categories. The deep web
contents may cover: a) geographically specific information, such as locators for chain
stores, businesses, and local services (e.g., doctors, lawyers, architects, schools, tax
offices); b) reports with statistics and analysis generated by governmental and nongovernmental organizations; c) product and price search, such as Google shopping, Bing
cashback program, PriceGrabber, etc.; d) a variety of other data, such as art collections,
public records, photo galleries, bus schedules, etc. In fact, deep web sites can be found
under most categories of the ODP directory [39]. These sources can be accessed by
simple keyword queries through a single search textbox, or detailed forms with many
select-menus and other refinement options. In addition, as online communities/forums
become universal for every subjects/topics, the content on these forums covers more
widely.
Three nice properties of deep web content are of high quality, very specific in nature, and
well managed. Consider CiteSeer [40] as an example. Documents stored in CiteSeer are
authored by professional writers and published in professional journals or conferences.
31
They focus on very specific topics or domains. Weather.com provides a national and
local weather timely and specific forecast for cities, as well as weather radar, report and
hurricane coverage. Both collections share the three properties.
The deep web is extremely popular on the current Internet, and it yields much more
valuable content than the surface web. Deep web content can easily provide many
different types of valuable services, such as Online TV guides, price comparison websites, locating cheap or used textbooks, driving direction guiding sites, tracking the prices
of your stocks and news about companies you are interested in.
2. Annotation Schemes
Annotation schemes are now popular in Web 2.0 applications or social networking
websites, such as Digg, Twitter etc. They enable users to add tags describing underlying
content (e.g., photos, comments, news) to enable better search over the content. The
Flickr Service by Yahoo! is a prime example of an annotation service for photo
collections. Ahn and Dabbish [41] took this idea to the next level by showing how mass
collaboration can be used to create high quality annotations of photos.
In a sense, annotation schemes require the users to provide very minimal structure,
specifically, only the annotations, which provide the values for “About” to every piece of
content. Obviously, annotations can be incorrect and inconsistent, i.e., use different
words to describe same piece of information.
3. Access and Spider Structured Data
32
The problems faced in the context of annotation schemes to access and collect data are
somewhat simple. Typically, the annotations can be used to improve recall and ranking
for resources that have very little meta-data associated with them (e.g., photos, videos). It
would be easy to solve the issue to discover deep web contents if structured data owners
can just make the data public on the Web. Currently only a few high-profile sites like
Amazon or YouTube try to provide public Web services or custom application
programming interfaces that open their databases and many more sites do not.
Currently there are no automatic ways about how to fill out any arbitrary forms. Actually
a lot of human efforts have to be involved to determine what information a particular web
form need. By analyzing forms’ elements and preparing set of possible values for each
element, we can interact with the web server which answers the requests of the form, and
send it the information that specifies the query plus other data the web server needs. We
can imagine that there are many of different types of forms. It’s almost impossible for
search engines to search them all, as a lot of human intervention is required during the
process.
While filling out that web form is difficult to handle, it isn't the only challenge to
accessing the deep web and it isn't even the toughest problem. It’s much harder to finding
the best, or most relevant, content. We will have to search multiple sources, collate the
results, remove duplicates and sort the remaining results by some criteria that is
meaningful to the user in some specific domain.
33
There are enormous amounts of valuable structured data on the Web today, and we will
finally address the challenges of making such data available and combining it with
unstructured data.
2.2
Life on the Web
Many of people now spend much time at social-networking sites like Facebook,
MySpace or LinkedIn. It is pretty easy to happily consume time in browsing, posting,
commenting on online communities. More and more activities in real life now are
virtualized on the Web: shopping, job hunting, dating, watching movies, listening to
music, seeking legal advice, gaming, chatting with friends, discussing big events, etc.
People even start to look for ways to abandon the virtual life and get actual life back, like
Web 2.0 Suicide Machine [43]. In this section, we will discuss online communities and
how people use latest peer-to-peer technologies to share resources, such as movies, music,
books, etc.
2.2.1
Online Communities
Based on Horrigan’s research [44], at least eighty-four percent of Internet users have
contacted or participated in an online community, and the growth in membership and
usage is expected to continue [45]. The common perception of online communities is that
they allow groups of people to share ideas and information. An online community can be
defined as an aggregation of individuals who interact around a shared interest or topic,
where the interaction is at least partially supported and/or mediated by technology and
34
guided by some protocols or norms, such as online forums. Figure 2.1 and 2.2 show the
screenshots of the main forum discussion page and the threads in a specific forum
discussion.
Figure 2.1 The screenshot showing the main forum discussing page
35
Although everyone can contribute an online community equally in theory, the vast
majority of online conversation or posts are conducted by a small group of web users,
which is less than ten percent of them. The rest of the community quietly sits back and
Figure 2.2 The screenshot showing the threads in a specific forum discussion
36
listen interactions as a mostly-passive audience that only occasionally injects a few
comments. Jakob Nielsen describes this phenomenon as the “90-9-1 rule” in
“Participation Inequality: Encouraging More Users to Contribute” [46]. It states:
•
90% of users are lurkers (i.e., read or observe, but don't contribute).
•
9% of users contribute from time to time, but other priorities dominate their time.
•
1% of users participate a lot and account for most contributions: it can seem as if
they don't have lives because they often post just minutes after whatever event
they're commenting on occurs.
Rubicon’s survey confirms the idea behind the “90-9-1” rule, although it does not support
its specific details, as Figure 2.3 [47]. A majority of web users sometimes contribute a
little bit even if it’s just an occasional comment. The truly silent lurkers are only 9% of
the web population. But the vast majority of content still comes from a small group of the
population.
Figure 2.3 How web users participate in online community?
37
There are several attributes that can help us to characterize online communities, including
Purpose, Platform, and People [48].
•
Attribute 1 – Purpose: The notion of purpose is the key to a virtual community as
the community itself is defined by shared purpose among community members.
People in the community understand and buy into its mission; they have a shared
vision for what it is, what it could become, and where it’s heading. They work
together to make progress toward a common goal.
•
Attribute 2 – Platform: the communication among members of an online
community is synchronous or asynchronous. For example, chat room technology
supports real-time communication (i.e. synchrononous interaction) whereas
email-based forums allow members to view and respond to messages at their
convenience rather than in real time (i.e. asynchronous interaction).
•
Attribute 3 – People: The people who interact with each other in the community
and who have individual, social and organization needs. Some of these people
may take different roles in the community, such as leaders, protagonists,
comedians, moderators, etc. on the earth, online communities provide channels or
platforms for people to communicate and interact online, thus the behaviors and
pattern of interaction become the most important attribute of a given online
community.
•
Attribute 4 – Policy: The language and protocols that guide people's social
interactions and contribute to the development of folklore and rituals that bring a
sense of history and accepted social norms. More formal policies may also be
38
needed, such as registration policies, and codes of behavior for moderators.
Informal and formal policies provide community governance.
In a thriving online community, people would like to share histories or share experiences
in the workplace, in their educational or vocational backgrounds. They may have worked
at the same company or attended the same school, or have the same professional
credentials, for example all those groups that are springing up within the LinkedIn
environment. Members feel a sense of belonging, feel “at home,” welcomed – free to
speak and act in ways that are authentic to who they really are. Members often have
shared values. The community exerts a magnetic attraction (may grow virally), and pulls
new like-minded people into its gravitational force field, who in turn attract others.
Online communities help companies to communicate directly with their customers and
gain valuable insight into what consumers think. There are several ways for companies to
use an online community.
1. Get customers involved in the business
Customers are likely to have extensive experience of the products, and how they are
actually used before they pull the trigger to get the products. A company would like to
check with the people who know its products best when a company plan to
commercialize a new product or idea, find out how people are using the product or find
out about how the company is different to the competitors. An online community can
provide a very appropriate platform between producers and customers, acting as a
customer voice and empowering internal teams with customer input and insight.
39
Feedbacks can be timely got from the online community which is able to represent the
customer inside the business.
2. Innovate with customers
In addition, an online community provides two way communication, and can be a great
way of both getting new ideas organically, and of working with customers on innovation
and co-creation. New ideas or features of products can be generated during ongoing
discussions between customers and producers by involving customers throughout the
innovation process, rather than just testing ideas at specific stages. An online community
makes it possible for internal experts and others with customers to talk with each directly,
and bring to the innovative ideas which might never have thought of before by experts
themselves.
3. Find out how customers interact
Customers are usually treated as isolated beings who can answer questions about their
habits and behaviors in traditional market research. It has a big problem as it doesn’t
consider the social context which is the most important aspect of any market decision.
Via an online community it is easier to observe the conversations people have. By
observing what they do and how they talk to their peers, we can figure out how they
discuss and evaluate products or competitor products, e.g. pros and cons, how they advise
other people, how they explain their decisions and opinions, what they choose to discuss.
4. Learn the language customers use
40
How to describe a product precisely and efficiently is a big challenge for many brands
and products. An online research community can help you analyze and draw insight from
the language people use.
5. Find answers to questions that the company didn’t even know to ask
Traditional market research usually designs a set of pre-defined questions to ask
customers, but how about those questions that are related to customers’ opinions and are
not ask in the traditional survey. The discussions in online communities will let the
company understand what customers talk to each other about, what they really think
about products and how they really talk about. It’s possible for the company to find out
what matters to them most and what they think about it. And most importantly they will
ask questions. Community members debate with each other, and thus generate ideas in a
well managed online community.
There are a lot of obvious research topics that focus on online communities, including:
the life cycles of different types of online communities; how online communities
organize themselves and develop norms, rules and policies; how to discover people’s
opinions on some specific topics or events automatically and efficiently; how to better
support sociability and usability in online community development; how to evaluate and
measure the success of different types of communities from participants’ and commercial
sponsors’ perspectives.
These topics mainly address online communities themselves. As online communities
become more and more popular, they actually are bridging online and offline social
41
networks, and people’s activities (such as opinions on events, comments on products,
arguments with other people, etc.) in online communities become projections of their real
life. For example, Facebook is used to maintain existing offline relationships or solidify
offline connections, as opposed to meeting new people. Given that online communities
enable individuals to connect with one another, it’s not surprising that they have become
deeply embedded in user’s life. Boyd [49] argues that MySpace and Facebook enable U.S.
youth to socialize with their friends even when they are unable to gather in unmediated
situations; and these Social Network Sites are “networked publics” that support
sociability, just as unmediated public spaces do.
2.2.2
Peer-to-Peer World
A peer-to-peer , commonly abbreviated to P2P, is any distributed network architecture
composed of participants that make a portion of their resources (such as processing
power, disk storage or network bandwidth) directly available to other network
participants, without the need for central coordination instances (such as servers or stable
hosts) [50]. Peers are both suppliers and consumers of resources, in contrast to the
traditional client-server model where only servers supply, and clients consume.
P2P applications can be used for many purposes such as Internet Telephony (Skype [51]),
distributed computing ([email protected] [52]) and content distribution (BitTorrent [53]).
Here we will focus on P2P applications used for content distribution because of their
greater impact on carriers’ traffic, and P2P was popularized by file sharing systems like
BitTorrent. The basis of P2P applications is a group of peers that collaborate in a
42
distributed system for the purpose of distributing content to one another. This does not fit
the traditional client-server model as each peer is a client and server at the same time [54].
Figure 2.4 Part of torrent file list for “Avatar” on the thepiratebay.org
According to the report from Mary Madden and Lee Rainie [55], about 36 million
Americans—or 27% of internet users—say they download either music or video files. 58%
of those who were current music downloaders said they had specifically pulled music
files from peer-to-peer services; 31% of these music downloaders said they were actively
downloading music files from these networks and 27% said they had used this source for
music in the past. When we isolate current video downloaders as a separate group, we
find that 32% of them admit to ever using peer-to-peer to download either music or video
files. Looking at current music and video downloaders in the aggregate, 33% report ever
43
using file-sharing networks to get music or video files. There is no doubt that peer-topeer file sharing systems and applications play as a significant role in people’s life on the
Web.
Figure 2.5 The screenshot downloading “Avatar.3D” using BitTorrent
File sharing peer-to-peer systems such as BitTorrent [53], Gnutella [56], and eMule [57]
have significantly gained importance in the last few years to the extent that they now
dominate Internet traffic [58, 59], way ahead of HTTP traffic. It has thus become of
primary importance to understand how, when, who use these applications to download
what kind of resources, which are people’s activities on the P2P network. Figure 2.4
shows the part of torrent file list for “Avatar” found on thepiratebay.org, and Figure 2.5
44
shows downloading “Avatar 3D” using BitTorrent, connecting to 30 seeders and 8
leechers.
In a P2P file sharing system, a distributed group of peers exchange contents in a
decentralized manner. Peers act both as servers and clients, providers and consumers. The
challenge in such system is how to help peers to find interesting content. Current popular
P2P file sharing systems usually offer search capability with the most usable indexing
functionality. There are three popular ways to provide search functions helping peers
locate the contents they are interested in. Firstly, like search engines, eMule [57]
maintains a series of centralized indexing services although the files themselves are
distributed. Another implementation is to broadcast the search requests over the network
connected peers, as does Gnutella [56]. Lastly, new P2P applications, such as Kazaa [60],
provide a kind of hybrid solution that does not have a single central index but kind of
"super nodes" that contain the index of content available in the proximity of the super
node.
To get the download completed as soon as possible, all the current P2P applications open
and sustain a large number of connections for each file downloaded and try to use as
much bandwidth as is available. This common feature results unbalance among the users
of an ISP. There is no easy solution to this issue as P2P applications transfer the data in
different ways.
There are several challenges to monitor the whole P2P network:
45
•
Difficult to locate download links: P2P network is world-wide range, and it’s not
like web sites or web pages which can be identified and located via URLs;
•
Network connection limit: even given a specific download link, for example an
eMule
link
“ed2k://|file|.Saving.Private.Ryan.1998.720p.BluRay.x264.DTS-
WiKi.mkv|13238649556|fbabcf591bf0e6403e1ab8a4fba7cac1|h=wdouxamsn46fx
65si4sxsjc5oxialvpt|/”, we cannot monitor all the peers that download or share
this link because of our network connection limit, thus result incompleteness;
•
Content Attack (Poisoning and Decoy): in one hand, more and more copyright
holders, such as movie studios, have been investing a lot of resources to
investigate technological solutions to prevent distribution of copyrighted materials
in p2p file sharing networks, and a popular technique that is applied now is
“poisoning” a specific item (movie, song, or software title) by injecting a massive
number of decoys into the p2p network, in the other hand, anyone can create fake
files and inject them into the p2p network; so even after we identify a peer to
download a specific item, we cannot assert it’s downloading the real contents.
•
IP Spoofing: it creates TCP/IP packets using somebody else's IP address. Routers
use the "destination IP" address in order to forward packets through the Internet,
but ignore the "source IP" address. In this case, malicious peers can tempt to form
file-sharing clusters with known user IPs, resulting that we may claim the known
user IP downloaded copyrighted contents wrongly.
46
2.3
Conclusions
In this chapter, we go through different types of data on the Web, explore the
characteristics of these types, and review the ways to approach the problems that are
resulted by these features. Most of the data on the Web are unstructured, and a lot of
researchers and companies have already provided methodologies and techniques to make
them structured and accessible. Although structured data on the web is not that easy to
access as unstructured data, because of the high value they are worth digging and
developing. Online communities become the major platforms for people to have social
life virtually, and they become another virtual society providing worthless materials for
people to research. P2P file sharing systems give users a fast and efficient way to switch
and broadcast contents. By monitoring p2p network, we can easily understand how, when,
who download what kind of resources, which are people’s activities on the P2P network.
47
CHAPTER 3
A FRAMEWORK FOR APPLICATION SPECIFIC KNOWLEDGE
ENGINES
As the enormous and yet exponentially increasing amount of web pages have been
distributed over the Web, it becomes more difficult to locate the exact information that a
user may be looking for due to the intrinsic nature of dynamic and unstructured web
information organization. A popular approach to overcome this problem is to use search
engines such as Google and Yahoo! which crawl web sites, download web pages and
create corresponding word/phrase indices. Although these general-purpose search
engines help people to unearth the relevant information from billions of documents easily
to certain extent, such search engines still bring some inevitable problems.
The direct problem is that the general-purpose search engines may often return a vast
amount of documents as a result of retrieval which includes many irrelevant web pages
and little indication as to which document is really the one the user needs. In this case,
people may have to click on some of the entries with the hopes that the topic of interest
will be within first few documents, or spend literally several hours navigating each of
them. This is because these general-purpose search engines create indices for diverse
topics and naive queries submitted by user often finds matches in many irrelevant pages
[61]. Generally, such search engines look for every occurrence of the word which was
typed into the search interface. Upon finding them, it lists each and every document
containing that word. However, the topic may only be mentioned within some of the
48
documents with no information of real value. Of course, people can get more relevant
results if they provide more specific and accurate information to the search engines, but it
will require the users to have much experience and sometimes the users are not sure how
to narrow the search queries. Actually 70% web search users only use only one keyword,
people are not accustomed to use sophisticated search functions like Boolean queries [62].
In some cases, when the topic related documents use certain words or phrases within the
text, and although the users typed in synonymous terms, they cannot be matched by the
search engines, and the worse case is that the keyword is simply misspelled.
Another issue with the general-purpose search engines is how to keep the web pages upto-date. Since the Web is huge and web pages are updated frequently, the search engines
have to refresh their indices periodically by crawling the Web and downloading web
pages, which is extremely resource and time consuming. It is very difficult for a single
search engine to cover the entirety of the Web and keep its index up-to-date at the same
time.
Perhaps more important, the page ranking technologies with which search engines
generate the order to list the results are operated by the commercial companies who own
these general-purpose search engines, e.g. Google. Although Google takes a "trust us"
approach to search and they will not skew their PageRank formula to favor certain sites,
but users have no way of knowing for sure.
Finally, since the general-purpose search engines treat every webpage the same, many
structured webpages lose semantic meaning after being indexed by the searching engines.
49
For example, the webpages on most online communities or social networking sites are
actually meaningful with topics, posters, post time, post content etc. In addition, the
general search engines have no way to monitor contents on the Internet beyond HTTP
protocol.
To address the problems above, it's highly desirable to develop an integrated,
topic/application-centered knowledge portal called Application-Specific Knowledge
Engines (ASKE) which supports more effective information retrieval and analysis as well
as collaboration and communication among users. For example, if a user who is
interested in the documents related to "sensors" in the "Intelligent Transportation
Systems" topic enters the single keyword "sensors", such knowledge portals are less
likely to return irrelevant pages.
In this chapter, we demonstrate the concepts of ASKE and propose a feasible approach in
many other different applications. The remainder of this chapter is organized as follows:
Section 3.1 presents the idea of knowledge portals and various applications of domainspecific search engines. We also review several approaches to building domain-specific
Web search engines. We present an overview of the system architecture of the ASKE
framework in Section 3.2. Thereafter, we explain the core components of the framework
in Section 3.3, and 3.4. The conclusions are given in Section 3.5.
3.1
Knowledge Portals and Applications
50
Currently the problem we are facing is not how to acquire information but that we
already have far too much information - we are overloaded. It's infeasible to cure the
problem by providing access to more information, or even by improving our efficiency in
generating information. One possible way is to produce, acquire, transmit, and manage
knowledge.
In November of 1998, a new concept called "Enterprise Information Portals (EIPs)" was
presented by Christopher Shilakes and Julie Tylman [63]. They defined EIPs as
"applications that enable companies to unlock internally and externally stored
information, and provide users a single gateway to personalized information needed to
make informed business decisions". EIPs consolidate diverse systems including Content
Management Systems, Business Intelligence, Data Warehouse/Data Mart, Data
Management, and other data external to these applications into a single system which can
"share, manage and maintain information from one central user interface". As the key
component of EIPs, the Content Management Systems process, filter and refine
unstructured or semi-structured information in diverse documents and formats and store it
in a data repository.
Based on the ideas of EIPs, Joseph M. Firestone presented "Enterprise Knowledge
Portals (EKPs)" [64] which is a type of EIP. An EKP is an EIP which is goal-direct
toward knowledge production, knowledge acquisition, knowledge transmission, and
knowledge management focused on enterprise business processes; and focuses upon,
provides, produces, and manages information about the validity of the information it
51
supplies. Simply, Knowledge Portals provide information about the topics of interests
effectively and efficiently, and also supply users with meta-information about what
information users can rely on for generating new knowledge. Actually this concept is not
restricted in enterprise applications only. Knowledge portals can be applied in various
domains, e.g. in scientific research fields, knowledge portals may correlate the
information in this domain with perfect accuracy and present it in a manageable form.
Compared the features of knowledge portals and search engines, we can find search
engines provide a feasible and flexible framework to implement knowledge portals of
diverse topics.
In recent years, some such domain specific search engines have been built. In scientific
research domains, CiteSeer [65] is a scientific literature digital library that aims to
improve the dissemination and feedback of scientific literature, and to provide
improvements in functionality, usability, availability, cost, comprehensiveness, efficiency,
and timeliness; ResearchIndex [66] is constructed for the searching of computer science
papers; Cora [67] is also a search engine for computer science research papers; NanoPort
[68] is a comprehensive Web portal to serve the researchers, scientists, and practitioners
in the nanoscale science and engineering (NSE) domain; AGROA [69] searches for
software components and builds a database classified by component; even there is
specialized search engine called Deadliner [70] which is used to find conference
deadlines. There are also some specialized search engines that are related to people's
daily life, for example: Camp Search (www.campsearch.com) allows the user to search
for summer camps for children and adults; Movie Review Query Engine
52
(www.mrqe.com) allows the user to search for reviews of movies; Travel-Finder
(www.trave-finder.com) allows the user to find travel information with activity, category
and location; Newstracker (nt.excite.com) and Moreover (www.moreover.com) are good
websites for the latest news; FlipDog (www.flipdog.com) searches job postings at various
IT company Web sites, and builds an up-to-date and powerfully searchable index of job
advertisements; Crafts Search (www.bella-decor.com) helps the user search web pages
about crafts and provides search capabilities over classified ads and auctions of crafts;
CMedPort [71] is a cross-regional Chinese medical Web portal to provide access to
Chinese medical information over the Internet.
Observing the building processes of these specialized search engines, we find that the
most popular approach to building domain-specific Web search engines is to only collect
and index the relevant documents available on the Web. This approach is composed of
the following main processing stages:
•
Collection of domain-specific web pages.
•
Information Extraction from the domain-specific web pages, involving two substages: parsing and indexing.
•
Data repository to store the parsing and indexing information into a common
database (MS SQL Server, MySQL etc.).
The other good approach is to reuse the existing indices of those general-purpose search
engines. Compared to the approach above, it requires less human efforts to build and
maintain the word/phrase indices. This approach is a kind of meta-search engines [72]:
53
the specialized search engine forwards the user's query to one or more general-purpose
search engine and catches the returned results. To guarantee that the documents returned
to the user are relevant to the topic of interest, two kinds of middleware (or agents) can be
implemented during the meta-search process. First method is that the domain-specific
search engine passes the original query to large-scale search engines, and eliminates the
irrelevant documents from the returned ones via a domain-specific filter [73]. The
drawback of this "filtering model" is that it downloads many irrelevant web pages and
spends much time to filter them out which consequently results the inefficient
performance. The other "keyword spice model" [61] does not filter documents returned
by a general-purpose search engine. Instead, it extends the user's query with a domainspecific Boolean expression (keyword spice) that helps narrow the original query better
and forwards the modified query to the general-purpose search engines. This model is
more efficient than the first one, but the relevance of the returned documents cannot be
guaranteed since it relies much on the quality of keyword spice. And when the domain
(category) is broader, more human expertise and efforts are required.
To promote the relevance of search results and the efficiency of building the specialized
knowledge portals, we propose to establish a new type of structured framework for
collecting and analyzing information in application specific domains, called application
specific knowledge engines (ASKE). The basic idea is to create a series of spider agents
which can collect data from heterogeneous sources, build up semantic data repositories,
and use a Knowledge Configuration File (KCF) to specify topics, keywords, searching
sequences and schedules for query processing. The key features of the proposed ASKE
54
are user specific, application specific, and domain specific and the process of ASKE is
report-motivated, semantic-based, and time-driven, instead of question-motivated and
query-driven, as in traditional web searching processes [74]. The important task of ASKE
is to offer both end users and applications a seamless access to knowledge contained in
heterogeneous data sources.
3.2
An Overview of the Framework for Application Specific Knowledge Engines
Figure 3.1 shows the framework and components of an application specific knowledge
engine, which contains two main phases: construction of Data Repositories and searching
by KCF with result presentation.
Phase 1 construction of Data Repositories: this phase focuses on how to build a highquality data collection that is comprehensive and relevant, including three main subphasess: data collection, data preparation, and data silo. In data collection phase, we use
Resource Identifier to locate data resources relevant to the application, Spider Agents
which can deal with different types of data to collect all data sources, Content Filter to
exclude noise and garbage data in the data collection; in data preparation phase, we use
Data Classifier to categorize collected data based on different file types, apply Parser and
Indexer to the data collection to build up indices, lexicon library, and searchable
databases; in the final sub-phase, we create semantic data repositories based on texture
documents and structured databases based on the data we collect in the last sub-phase via
Ontology Developer and Metadata Extractor.
55
Phase 2 searching by KCF with result presentation: with high quality semantic data
collection, the components in this phase try to help users to locate information accurately
and easily; search is conducted by KCF or in semantic ways; search results are presented
to users with different components including list, keyword suggestion, summarization,
categorization, and visualization.
3.3
Construction of Data Repositories
The construction phase contains mainly three sub stages: data collection, data preparation,
and data silo.
3.3.1
Data Collection
In building an application specific knowledge engine, high-quality collections will be
main factor that determines its usefulness and efficiency. ASKE should contain as many
as relevant, high quality documents and as few irrelevant, low-quality documents as
possible. To address this need, there are two general approaches to collecting relevant
Web documents: manual selection and automatic Web crawling.
56
Figure 3.1 The framework and components of an Application Specific Knowledge Engine
1. Current popular approaches to collect documents
For example, In the National Library of Australia's PANDORA (Preserving and
Accessing Networked Documentary of Australia) project (http://pandora.nla.gov.au/), all
57
Websites were reviewed and manually selected to make sure that only relevant sites were
archived. Also ACT (Anti-Terrorism Coalition) applies a decentralized mechanism to
identify terrorist websites, e-groups and forums manually by agents and users spreading
all over the world, and has compiled a database of terrorist and pro-terrorist websites and
e-groups (http://atcoalition.showsit.info/). Through manual selection, genuine relevant
information could be collected.
Alternatively,
in
the
National
Library
of
Sweden's
Kulturarw
project
(http://www.kb.se/kw3/ENG/Default.htm), a Web crawler (also referred to as Internet
Spiders or robots) was used to automatically download Websites with .se country domain
names or from Web servers known to be located in Sweden. Web documents are spidered
efficiently via automatic Web crawling. In this approach, some algorithms have been
developed to guide Web crawlers to locate Web pages and predict whether a URL is
likely to be the relevant resource, e.g. HITS [75], PageRank [76], etc.
However, neither of the above two methods could be directly applied here. The manual
selection method is not efficient and often results in low coverage. The automatic Web
crawling method often introduces noisy into the collections, resulting in low precision.
These two methods offer few efforts to trace and monitor websites’ transitions and
guarantee update-to-date high quality data collections.
58
Figure 3.2 The structure of Resource Identifer
2. Resource Identifier
We propose to use a recursive collection building procedure which combines both
manual selection and automatic Web crawling methods. In this method, a limited number
of seed URLs are first identified through careful and systematic manual selection. These
URLs are then captured using automatic Web crawlers. Favorite link pages (a specific
type of Web pages where the Web masters list the URLs of some other Websites that
they recommend.) are extracted from the captured Websites. Manual selection is
performed again on the out-going URLs listed in those favorite link pages which are very
likely to be URLs pointing to other domain or application related Websites. Then, we
repeat the whole process on the newly identified seed URLs to build the collection.
This procedure is conducted by the Resource Identifier. Figure 3.2 shows the structure
of Resource Identifier. It is responsible for locating the domain relevant resources, e.g.
59
the sites that are known to provide specific contents, web directories that are dedicate to
gathering related sites and web pages in this application. It extracts the favorite links of
those well-known sites which are believed to be related to the topics of interest. Also a
lexicon reflecting the key concepts and terms as well as their usage in the domain is built
by domain experts. It is apparently helpful to collect as many specific terms as possible in
the lexicon. The Resource Identifier sends queries that are created based on the lexicon to
multiple general-purpose search engines and retrieves the top 100 or 200 results as the
part of resources in our content collection. These top results are usually of high-quality
and much diversity, thus greatly improve the quality of the collection.
In the mean time, users can configure those favorite sites as the seed URLs in KCF so
that the data collection is specialized for their own interests. In addition, the resulting
resources are classified into different categories (e.g. News, Journal Papers, and Forums
etc.) for generating reports in the future.
3. Spider Agents
After the resources of high quality have been identified, the document collecting is done
automatically by Spider Agents. A Spider [77] is a program that automatically fetches
Web documents. It is called a spider because it crawls over the Web, and another term for
these programs is "crawler". Because most Web pages contain links to other pages, a
spider can start almost anywhere. In our approach, to make sure that fetched pages are
really relevant to the domain, the spider is limited within particular hyperlinked resources
from the Resource Identifier. To fetch more pages in shorter time, many spiders work in
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parallel. Compared to large search engines, our data collection is small, thus generally we
only start 10-20 spiders to crawl the Web.
There are several issues required to be solved in the design and implementation of spider
agents, including what to spider, how to spider, and how often to spider (frequency). The
Resource Identifier resolves the first issue. It provides spider agents resources (URLs,
etc.) to collect data. As the relevance of the resources to the domain and user expectation
which is specified in KCF varies, spider agents cannot treat all the resources the same, e.g.
some resources have to be monitored more frequently to get real time data. In the ASKE
framework, we borrow the idea from physical sensors to construct Sensor Network to
build up spider agents.
•
Social Sensor Network
Sensors are popular in people’s everyday lives. They can provide information about a
car’s condition, can enable smart buildings, can locate friends’ locations, and are being
used in a various mobile applications. Generally, sensors provide information about
various aspects of the real world. Similarly, “sensors” can also be used on the virtual
world – the Internet to “sense” the Web data. As information appears and spreads on the
Web, we build up various “social sensor networks” (see Figure 3.3) in different circles
based on the categories or organizations of resources and the relevance among them,
actively monitoring the resources by collecting data in real time.
The Internet is a huge dynamic system. Given a URL, the content in this URL may
change frequently as time goes by, such as responses (comments, replies etc.) are added
61
to the Web page. In P2P applications, new ed2k links or torrents for a given title can
appear or disappear at any time, and users that download the corresponding copyrighted
contents are more inconstant. By periodically monitoring and scanning resources, we
may discover if new data on the Internet is interested by users in real time. The list of
monitored resources and scan intervals can be customized by users via KCF.
Category/
Organization 1
Category/
Organization 8
Category/
Organization 2
Seed 1
Category/
Organization 3
Seed 8
Seed 2
Seed 7
Category/
Organization 7
Domain/
Application
Seed 8
Seed 3
Seed 4
Seed 5
Category/
Organization 4
Category/
Organization 6
Category/
Organization 5
Figure 3.3 The architecture of social sensor networks
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In social sensor networks, scan intervals can also be changed adaptively in the framework
by calculating activity levels of resources. The closer a resource is to the center, the
sooner the resource is scanned. For example, initially we monitor a torrent file “Avatar
2009 DVDScr H264 AAC-SecretMyth (Kingdom-Release)” every one hour. In the first
hour, we found 34 peers downloading this torrent, and in the second hour 175 peers
downloading this torrent which are 5 times of the first hour. In this case, the spider agents
would reduce the period from 1 hour to 12 minutes as this torrent becomes a “hot”
resource.
Although Web spider technology is kind of mature to handle most of unstructured web
pages, as the new technologies and applications are applied in the Internet, the traditional
Web spiders cannot handle a lot of new applications, such as Ajax [78], online
communities, and P2P applications.
•
AJAX Applications
The traditional spidering process is as the following: connecting to a server, pulling down
the HTML document, parsing the document for anchor links to other HTTP URLs and
repeating the same process on all of the discovered URLs. Each URL represents a
different state of the traditional web site. AJAX (shorthand for asynchronous JavaScript
and XML) is a group of interrelated web development and communication techniques
running on clients to give users the same interactive experience as desktop applications.
With Ajax, web applications can retrieve data from the server asynchronously in the
background and update the part of the existing web pages without refreshing or reloading
63
the whole page. Ajax techniques make web applications feel like local applications with
rich interactivity and dynamics.
In a Web page with AJAX enabled, the page is loaded from the web server in the
beginning. When the user starts to interact with the Web page, like clicking buttons,
selecting options etc, some of the page content is changed in the HTML document, which
is dynamically inserted by Javascript during the interactions process. Not only form
elements can trigger Javascript events, but also anchor links which usually point to other
URLs. After the Web page is fully loaded, the series of Javascript events that are
triggered define the states of the application. For AJAX applications, the traditional
spider can only reach or fetch a small fraction of the content and is unable to index any of
the application's state information and dynamic contents.
Obviously, traditional web spiders would not work with AJAX applications. To solve this
issue, new spiders that can understand not only how to traverse among links and parse
HTML format, but also the structure of the document as well as the Javascript that
manipulates it have to be developed. To be able to research the complete states of an
application no matter how deep the state is, the new spiders also need to be able to
identify and trigger events within the document to reach each possible state and simulate
the paths that might be taken by a real user.
As we discuss above, traditional spiders run in a “protocol-driven” way, which does not
work they meet an AJAX enabled page. This is because all target resources are generated
by JavaScript codes dynamically and are embedded in the Document Object Model
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(DOM) context. It is important to both understand and trigger this DOM-based activity.
In the process, new spiders have to implemented in another way called "event-driven"
[79] . It has the following three key components:
o Javascript analysis and interpretation
o DOM event handling and dispatching
o Dynamic DOM content extraction
To develop a complete new Javascript interpretation engine would be a very tough and
time consuming job. By using a modern browser such as Firefox or Internet Explorer (IE)
as the underlying platform, we implemented our AJAX-enabled, event-driven spiders in
an efficient way. There are a couple of similar tools available to utilize the existing
browsers, such as Watir [80] and Crowbar [81]. These tools allow us to control Firefox or
IE from our own codes, thus to extract data after any Javascript event is triggered.
Watir, pronounced water, is a library that enables automating web browsers using Ruby.
It supports most of current modern browers, such as Firefox and Safari. The Watir API
allows users to launch a browser process and then directly extract and click on anchor
links from Ruby application.
Crowbar is another interesting tool which uses a headless version of Firefox to render and
parse web content. By providing a web server interface which wraps the browser, it
allows us send simple GET or POST requests from any language or even simple
command line tools such as curl and wget, and then fetch and parse the results as needed
just as traditional spiders do.
65
We chose Crowbar instead of Watir after careful comparison. Crowbar is language
independent and simple to integrate into an existing mature traditional crawler design to
extract page information that would only be available after a page has completely loaded.
Watir, on the other hand, provides deeper, interactive, and direct access to the browser,
thus is easier for users to control and trigger Javascript events. The main issue is that we
have to develop a brand new spider with more complicated logic to discover states and
additional Ruby exploration.
•
Online Forums
Most online forums look like same as common Web pages. But besides the regular
spidering process, we have to consider some other issues when we design the spider
agents specifically for online forums.
1) Apply for forum membership. Many online communities require membership to access.
We need to create a common user name and then send the application request to forum
masters. Once the application is approved, we use the user name and password to access
the forums. In some cases, the forum masters could be very selective. It may take a
couple of rounds of emails to obtain access privilege.
2) Handle membership access in spidering. We manually access the forums for the first
time using the authorized user name and password. The forums store our access
information in permanent or temporary cookies on our local computers. When spidering
the forums, we direct our spider program to use the cookies with authorized membership
information to access the forum contents. We follow Robot Exclusion Protocol. It is a
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method that allows Web site administrators to indicate to visiting robots which parts of
their site should not be visited.
3) Handle different forum software. Online communities are implemented using different
forum software packages with different Web application languages. To download these
forums with different packages, the spider program needs to know the pattern of URLs
created by such software. Thus, once the forum software packages are identified, URL
templates need to be created. The spider program then uses the templates to generate
parameters required by corresponding forum software packages. Table 3.1 shows forum
software packages used in most online communities and their corresponding application
languages.
Software Packages
Application Languages
Software Packages
Application Languages
Discuz!
PHP
PHPWind
PHP
Crosstar
PHP
phpBB
PHP
DCForum
PHP
rafia
PHP
ezboard
CGI
vBulletin
PHP
IM
PHP
WebRing
CGI
Invision Power Board
PHP
WebWiz
ASP
newbb
PHP
YaBB
PHP
Table 3.1 Popular Forum Software Packages
4) Identify patterns of forum messages. Besides the different URL template needed for
each forum package, different syntax and formats in forum messages also need to be
captured for different forums software packages. In order to correctly extract important
information such as thread titles, authors, and post dates, the spider program need to
67
recognize these important fields from the returned messages. We create separate parsers
for each forum software listed in Table 3.1 to extract the most essential information from
forums of different formats. For example, for messages from vBulletin-based forums, our
vBulletin parser is able to extract the message titles by seeking the title meta tags (“<!icon and title>” and “<!- /icon and title>”) in the message body.
Protocol
Type of Server
http://
HTTP server
https://
Secured HTTP server
mms://
Microsoft Media Player stream server
mmst://
Microsoft Media Play TCP stream server
pnm://
Older version of Real Player stream server
rtsp://
Real Player stream server
ftp://
FTP server
Table 3.2 Different Types of Download Servers
5) Handle external links and local attachments: In addition to the textual contents of
forum messages, multimedia materials (e.g., images, video/audio clips, etc.) posted by
forum participants in their messages are also very important. This material can be
uploaded to the forum server as attachments or can be hosted on another server with their
URLs posted in the forum message as external links. We set up our spider program to not
only download Web pages, but also download textual documents (e.g., plain text files,
MS Word files, PDF files, etc.), multimedia documents (e.g., images, animations,
video/audio clips, etc.), archive documents (e.g., ZIP files, RAR files, etc.), and other
non-standard files (files with extension names not recognizable by Windows operating
68
system). The external materials are hosted on different types of servers that require
different download methods. The spider program monitors the protocol sections of the
external URLs to decide which download method to use. Table 4 summarizes the types of
download servers that the spider program recognizes.
6) Handle Multiple Views of the Same Content: Forums sometimes support views of the
same content. For example, in vBulletin-based forums, a thread can be displayed in three
views: linear view, threaded view, and hybrid display. Each view is spidered as a unique
document although they contain the some content. These redundant documents need to be
filtered based on the URL patterns to keep the collection concise. Furthermore, in a
forum, there are pages that do not contain meaningful information, for example, pages
where users start a new thread or pages where users post replies to an existing thread.
These non-meaningful pages also need to be filtered out.
7) Prevent Spiders from Vicious Links: Some forums may contain hyperlinks that trap a
spider program in a loop or dynamically generate infinite number of new links (e.g.
calendars, forum internal search engines, etc.). If the spidering process does not finish in
a reasonable time limit or after a reasonable number of documents are downloaded, we
need to examine the spidering log and identify the vicious links. Identified vicious links
are excluded in future spidering process.
8) Prevent Spiders from Being Blocked: Forums may block an IP address if too many
requests are sent from the IP address in a short period of time. We need to set a random
time delay between hits and make the spider program mimic human browsing behaviors.
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Some forums only allow specific types of Web browsers (e.g., Microsoft Internet
Explorer 8.0, Firefox 3.6, etc.) to access their contents. We need to set up the spider
program such that it mimics a certain Web browser to access the target forums.
9) Update Forum Collection: Forum contents are constantly being updated. The spider
program needs to revisit the target forums periodically to download new threads and
posts.
In summary, the proposed forum collection approach takes advantage of both human
experts’ knowledge and automatic Web spidering techniques. It reduces human
intervention and increases the efficiency of forum collection and monitoring process.
•
P2P Applications
To collect data about how people use P2P applications to download resources and
monitor their behaviors, the best way is to be part of P2P network. We create multiple
spider agents implementing different P2P protocols (e.g., BitTorrent, eDonkey, and
Gnutella). Since spider agents would not really download/upload contents, they act as
hidden “sentinels” with pretty light weight mimicking different roles: regular or “neutral”
peers, malicious peers, and seed servers.
As ‘neural” peers, the agents mimic the behavior of P2P peers by implementing the same
discovery and download protocols, exhibit similar download speeds, arrival and departure
rates as the regular clients, and in the mean time collecting their neighbors detail
information, e.g., IP address, shared files (name, size), .
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As “malicious” peers, the agents mimic the behavior of malicious peers by sending out
probes to their neighbors at the same rate as other malicious peers. The agents actively
search, study and run the software that malicious users use, and also use two-way
communication packets to verify the malicious peers’ IPs.
Finally, the agents serve in the capacity of BitTorrent Tracker servers and eDonkey index
servers, and share real copyrighted contents to figure out who download them from us.
Since the spider agents are light-weight, using very little machine resources (CPU time,
memory etc.), we can spawn hundreds of instances on one server with high band-width
network connection. By assigning servers over USA in different locations, we can expect
to monitor P2P activities in USA in real time.
4. Content Filters
Although the Resource Identifier tries to only get the relevant information, some noise
inevitably exists in the collection. And sometimes the noise is brought by the spiders that
catch some irrelevant information. The common used filtering techniques include:
• Domain experts manually determine the relevance of each Web page.
• In the simplest automatic way, the relevance of a Web page can be determined
by the occurrences of particular keywords (e.g., health care bill ) [82].
• TF*IDF (term frequency * inverse document frequency) is calculated based on
domain-expert created lexicon. Web pages are compared with a set of relevant
71
documents, and those with a similarity score above a certain threshold are
considered relevant [83].
• Text classification techniques, such as Naive Bayesian classifier [84] [67] and
Support Vector Machine (SVM) [85].
Our approach is to calculate the sum of two values of a given webpage via the terms and
combinations of the terms in the domain lexicon:
o TFIDF(p) = Sum of TF*IDF of the terms in page p found in domain lexicon
o Title(p) = Number of terms in the title of page p found in domain lexicon
The relevance of each document is calculated and ranked. The quality of the collection
can be guaranteed by a relevance threshold which is calculated from a set of manual
identified relevant documents.
3.3.2
Data Preparation
After collecting Web documents and different kinds of data, we need to process the
collection and make them accessible or searchable.
1. Data Classifier
Generally, the collection contains diverse types of documents, e.g. web pages, PDF and
Word files, images etc. In some domains, especially scientific domains, people would
like to find information in different types separately. The "Data Classifier" classifies the
documents based on their file types.
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2. Parser
The Parser converts different types of files into the plain text format, so that later on the
Indexer can process them. For webpages, the Parser recognizes the html tags and words.
The html tags, style sheets, and scripts are stripped off. For other types of documents, like
WORD or pdf, the parser utilizes tools (e.g., pdf2txt, ABC Amber Text Converter, etc.).
For multimedia objects, we mainly use the texts around them.
3. Indexer
The Indexer is responsible for tokenizing the Web data into words, and storing the
relationships between words and documents. Various information techniques are applied
in the Indexer, e.g. stop words and fuzzy indexing. To support the fuzzy searches (for
example, a search for "running" also finds "run" and "runs"), several stemming
algorithms are used: the Porter Stemming algorithm [86], Soundex, Metaphone and
Double Metaphone algorithm [87]. It is a good practice to create both a normal (exact)
index and a fuzzy index and allow the search interface select which index to use. The
indexer doesn’t need to process any P2P data, since they are already structured. Finally
the resulting searchable indices are stored into a database for further document retrieval
and analysis.
3.3.3
Data Silo
After the first 2 sub stages ‘data collection’ and ‘data preparation’, we now have the
collection of all available data sources such as semi-structured textual documents,
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structured data in the form of databases. In this stage, we need to extract high quality
metadata from heterogeneous sources in an automated manner to build up the semantic
data repository, which is part of “Data Silo” combined with previous available data
collections.
The Semantic Web [88] is the vision of the next generation of the World Wide Web, in
which information is given well-defined meaning and thus becomes understandable and
consumable not only for humans, but for machines as well. Currently there are some
semantic web portals focused on certain domains that have been developed. In these web
portals semantic mark-ups are gathered, stored and accessed, for example MindSwap [89],
Knowledge Web [90], and MuseumFinland [91]. Semantics are added to the contents and
services provided by these semantic web portals extending the traditional idea of web
portals. MindSwap and Knowledge Web are examples of research projects based
semantic web portals. They describe and present relevant domain knowledge based on
back-end semantic data repositories. As the first semantic web portal, MuseumFinland
aggregates heterogeneous museum collections. The underlying metadata is extracted
from distributed databases by mapping database schemas to the shared museum
ontologies. The portal provides a view-based multi-facet searching function and a simple
keyword searching function. Via multi-facet searching, users can filter results using
museum category hierarchies. The keyword searching function, in another way, matches
the keyword with the available categories and then uses the category matches to filter
results.
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Two components in this stage have been developed: Ontology Developer and Metadata
Extractor.
1. Ontology Developer
An ontology is a formal specification of a shared conceptualization [92]. Ontologies
capture the structure of the domain, i.e. conceptualization, including the model of the
domain with possible restrictions. The conceptualization describes knowledge about the
domain, not about the particular state of affairs in the domain. In other words, the
conceptualization is not changing, or is changing very rarely. Ontology is then
specification of this conceptualization - the conceptualization is specified by using
particular modeling language and particular terms.
There are a lot of existing ontologies in our daily life, for example:
•
Taxonomies on the Web: DMOZ (Open Directory Project), Yahoo! and Google
categories
•
Catalogs for on-line shopping: Bing Stores, Amazon product catalog
•
Domain-specific standard terminology: Unified Medical Language System (UMLS),
UNSPSC - terminology for products and services
Usually a domain specific ontology is developed by domain experts. Figure 3.4 shows the
ontology development process, which includes 7 steps. These steps may repeat during the
whole process.
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Figure 3.4 Ontology-development process
•
Determine domain and scope: this step tries to answer questions, like what the
domain that the ontology will describe and cover is, how we are going to use the
ontology, for what types of questions the ontology could answer, and who will
use and maintain the ontology, etc.
•
Consider reuse: are there any available ontologies that have been validated
through use in other different applications?
•
Enumerate important terms: find out the terms and the properties of these terms,
and how we are going to describe these terms.
•
Define classes and the class hierarchy: A class can be defined as a concept in the
domain or a collection of elements with similar properties, and classes usually
constitute a taxonomic hierarchy.
•
Define properties of classes: include “intrinsic” properties, “extrinsic” properties,
relations to other objects etc.
•
Define constraints: property constraints that describe or limit the set of possible
values for a slot, and constraints that defined for class inheritance.
•
Create instances: the class becomes a direct type of the instance, and assign
property values for the instance which should conform to the constraints.
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The Web Ontology Language (OWL) which is a family of knowledge representation
languages including OWL DL, OWL Lite, and RDF schema, is used this component to
author ontologies. In this component, domain experts follow the process we describe
above to develop ontologies using RDF/XML syntax.
Figure 3.5 The architecture of Metadata Extractor
Developing a new ontology for a specific domain or application is one way. The
alternative way is to find out existing validated ontology for this given domain. There are
a lot of available sources for ontologies online, e.g., semanticweb.org, Swoogle
(http://swoogle.umbc.edu/).
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2. Metadata Extractor
The metadata extractor is responsible for the extraction of high quality semantic data
from the source data. How to ensure the quality of the extracted data becomes a
challenged issue. In the metadata extractor, we design several ways to extract high
quality data. First, it combines the context in which an entity is mentioned in order to
determine its type and thus reduce ambiguities. Second, a verification engine is
developed in the metadata extractor, and it checks the validity of any new derived
metadata by comparing them against a trusted domain knowledge collection and the
related information on the Web. Finally, we keep the metadata extractor automatically
running whenever new data is pumped in, thus ensuring that the semantic metadata is
always up to date. Figure 3.5 shows the architecture of the metadata extractor. It contains
two key components: an automatic and adaptive metadata extraction tool used to marksup textual sources; a semantic transformation engine, which convert and format data from
original representations into new formats based on the specified domain ontology.
•
Metadata Extraction
To address the issue of adaptive information extraction, we use Arizona Noun Phraser
[93], a named entity recognition (NER) tool that provides an adaptive service. The AZ
Noun Phraser is made up of three major components, a tokenizer, a part-of-speech tagger,
and a phrase generation tool. It uses textual documents or files as input and generates a
list of the named entities mentioned in that document. It relies much on domain
ontologies and a repository of lexicon entries to process heterogeneous documents.
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For the purpose of converting the extracted data to the specified domain ontology, a
semantic transformation engine has been developed. It generates semantic relations along
with semantic data entries among the named entities from Noun Phraser, and the
specification of domain specific knowledge (i.e. lexicons, which are used in the
Resource Identifier). The lexicons will be later used by the verification process also.
The inputs for this component are structured sources from Noun Phraser and
transformation instructions.
•
Metadata Verification
The goal of the verification engine is to check that each entity has been extracted
correctly by the extraction components to ensure the high quality of extracted data. The
verification process consists of three complex steps which involve several semantic web
tools and some resources.
Step1: Checking the internal lexicon library. The lexicon library maintains domain
specific lexicons, such as abbreviations, and records the mappings between strings and
instance names. The verification engine considers any abbreviation is matched to the
corresponding entity.
Step2: Querying the semantic web data repository. This step queries the existing
semantic web data which is acquired before and should be correct. The querying process
is conducted by a number of string matching algorithms (such as Rabin-Karp string
search algorithm, Knuth-Morris-Pratt algorithm, Boyer–Moore string search algorithm)
to deal with minor errors and typos in the data. If there is a single match, which means
79
the current data entry exists in the semantic web data repository already, the verification
process ends immediately. However, there could be more than one match. We have to
utilize contextual information to address the ambiguities.
Step3: Investigating external resources. When the second step cannot find a match for
this entity, external resources such as the Web or existing semantic resources are applied
to identify whether the entity is erroneous, which should be removed or corrected. Here
we make use of PANKOW classification service and WordNet [94], to determine the
proper classification of the entity. If the entity is not classified correctly, we have to
compare other major concepts of the domain ontology with the Web-endorsed type to
find an appropriate classification for the entity in the domain ontology. Otherwise, we
can just create a new instance and add it to the repository directly.
3.4
Searching by KCF with Result Presentation
When a collection is ready, the next phase, consisting KCF Processing, Keyword Search,
Semantic Search, and Result Presentation, is conducted to support searching and analysis
of the results.
3.4.1
KCF Processing
Quite often, it is impossible to create an application that works for all users. Each user
may have his or her own preferences or requirements and the application needs to be able
to adjust accordingly at runtime to satisfy the user. One of the easier approaches is to use
80
configuration files to remember the user's preferences. Knowledge Configuration File
uses XML to describe users’ preferences and customize the system’s behaviors.
KCF includes
mainly
4
sections:
user
profile,
favorite
resources,
favorite
topics/categories, and queries.
1. User Profile
This section stores a user’s personal information, such as name, email, and specialty etc.
<profile>
<salutation>*</salutation>
<first_name>*</first_name>
<last_name>*</last_name>
<email>*</email>
<specialty>*</specialty>
</profile>
2. Favorite Resources
In this section, users provide favorite resources that are monitored by the system
frequently. The resources could be a URL, an ED2K link, a torrent file link, or a title for
copyrighted contents. Each resources have three attributes: topic, period, and type. Topic
attribute states the topic or category this resource belongs to; Period attribute customizes
the frequency the Spider agents monitor the resource; and Type attribute points out what
kind of the resource is, such as URL, ED2K link etc.
<favorites>
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<favorite topic="" period="" type="">*</favorite>
<favorite topic="" period="" type="">*</favorite>
…
<favorite topic="" period="" type="">*</favorite>
</ favorites>
3. Favorite Topics/Categories
Favorite Topics/Categories section stores the topics or categories that the user is
interested in. This helps the keyword search and semantic search only find related
information in these predefined categories. Each topic is described with name,
description, and a series of keywords. If a topic is a sub-topic of another topic, it also has
a parent_id attribute to describe which parent topic it belongs to.
<topics>
<topic id= "1">
<name>*</ name>
<description>*</description>
<keyword>*</keyword>
<keyword>*</keyword>
…
<keyword>*</keyword>
</topic>
<topic id= "2" parent_id= "1">
<name>*</ name>
<description>*</description>
<keyword>*</keyword>
<keyword>*</keyword>
82
…
<keyword>*</keyword>
</topic>
</topics>
4. Queries
In this section, users can store searching sequences and schedules, which help minimize
the efforts to create complex search queries manually in one search.
<queries>
<query id="">
<schedule>
<minute>0-59</minute>
<hour>0-23</hour>
<day_of_month>1-31</day_of_month>
<month>1-12</month>
<day_of_week>0-7</day_of_week>
</schedule>
<keyword order="1" type="">*</keyword>
<keyword order="2" type="">*</keyword>
…
<keyword order="n" type="">*</keyword>
</query>
</queries>
The “schedule” section follows the configuration rules in common CRON jobs. It defines
how the searches are performed periodically. The keyword sections define the keywords
and the sequences that will be searched in the system. The Type attribute in keyword
83
sections defines how the keywords will be searched, such as “phrase searching”,
“Boolean AND searching”, “Boolean OR searching”.
The KCF Processing module mainly performs the following tasks:
•
Parses the preset knowledge configuration file which is in XML format, extracts
topics, keywords, searching sequences and schedules;
•
Following the searching sequences and schedules, generates corresponding queries;
•
Parses each query into several words with Boolean relationships or phase searching;
•
Forwards the each query to the data repository to find search results recursively;
•
Synthesizes the results from each query based on the relationship configured in KCF.
3.4.2
Semantic Search
Keyword search is currently most popular way to identify resources in search engines, as
it provides a simple way to query data. Compared to keyword search which is based on
keyword comparison, semantic search provides better results and performance for
keyword searching by using the underlying ontologies and metadata collection built in
semantic web portals. Most of current semantic search technologies [95-97] mainly focus
on enabling semantic entities search, which totally neglect the semantic relations among
entities which are generated by manual annotation and automatic/semi-automatic
extraction.
84
Here we use the keywords and topic defined in KCF as input to do the semantic search.
The topic is used to limit the search procedure in some specific domain. The procedure
will look like as the following:
1. Understand the input
A keyword may have different meanings in different context. It can be general concepts,
or instances entities, or even literals (e.g., strings) of certain instances. This step employs
string matching algorithms to match the keyword against literals, ontology concepts, and
instances step by step and finds out the keywords falls into which category: general
concepts, instances entities, or literals. Then we can know which entity is matched and
the similarity for the input keyword.
2. Get related instances
If the keyword is a general concept, the related instances are the instances of the matched
concepts. If the keyword is an entity, the related instances are the matched instance.
3. Get search results
The search results are actually those instances that have direct or indirect relations with
the matched instances. If the instances are associated directly in explicit triples, we call it
a direct relation, while indirect relations are the ones which can be derived from the
explicit triples. Relations can be associated together when they share a same instance,
which is called Mediator. The more mediators exist in between, the weaker the derived
relation is.
4. Rank search results
85
Search results are ranked based on string similarity, and semantic relation closeness. The
string similarity is calculated in Step 1. The semantic relation closeness represents how
close the semantic relations are between the instances in a search result and the matched
instances. To quantify the semantic relation closeness, we count all the relations a search
result has with all of the matched instances, for example, we give the direct relations the
weight 1.0, and the derived ones 0.5, 0.25 etc.
3.4.3
Result Presentation
The Result Presentation module is the key role to generate reports and send them to users.
It contains five functional components: the Document List, the Keyword Suggestion, the
Summarization, the Aggregation and the Visualization. Consolidating these components,
the system will present users an integrated, comprehensive, systematic and up-to-date
report.
1. Document List
It will list all the results from various sources one by one from the high relevance to the
low one, just as general-purpose search engines do. In reports, the address of each
document, size, modified time, short description and cached location are presented to the
users.
2. Keyword Suggestion
86
A lot of research work has been done in the area of keyword suggestion. There are
mainly three different techniques for generating keywords: query log and advertiser log
mining, proximity searches and meta-tag crawlers. The first technique is popularly used
by search engines, as they have huge amount of query log data and advertiser log data.
By mining the logs, they can figure out what keywords are usually queried together with
the initial keywords (we call co-occurrence relationship between the keywords). For
example, Google's Adword Tool [56] mines advertisers' log to determine keywords they
searched. Bartz [98] proposed a new method based on collaborative filtering which
involves the clicked URLs as a factor, and uses the relationship between the URLs and
the query terms to generate new keywords. Obviously, it’s possible that the suggested
keywords are only the ones appear the logs most frequently, which can be unrelated to
the initial keywords.
Most of the commercial tools use the second technique - proximity based methods - for
generating keywords. The suggested keywords generated by search engines are used as
the seed keywords. The tools then append words found in its proximity.
Keyword Suggestion component helps users to expand and optimize ineffective
keywords by showing users related keywords that include the search terms or phases. As
we have created semantic data repository, we implement keyword suggestion component
via computation of semantic similarity by using a diffusion process on a graph defined by
lexicons and co-occurrence information [99].
3. Document Summarization
87
Summarization can be performed on individual documents to provide summaries which
are intended to give quick previews of Web documents [100] by reducing the size and
complexity of the documents and offering concise representations of the documents [101,
102]. For example, for a long web document, via document summarization technique the
key sentences are extracted, thus to allow users to make a decision whether a document is
of interest without reading the document in detail based on the relevance of the document.
There are mainly two methods to text summarization, including text extraction and text
abstraction. Text extraction, as the name suggests, extracts original sentences from a
document to construct a summary. The summarization involves the following related
techniques: sentence evaluation, segmentation or topic identification, and segment
ranking and extraction. Text abstraction, in the other hand, summarizes a document using
generated grammatical sentences. Obviously, a great deal of document processing and
computation has to be involved. From the review, recent research trend of document
summarization focuses on the text extraction method [102, 103].
The Summarization component is responsible for summarizing texts from the search
results. It reads texts from the resulting Web documents, decides which sentences are
important and which are not, and adjusts the summary ratio.
4. Document Categorization
Users are often frustrated when facing a huge amount of documents, so it is desirable to
gain an overview of the result documents, explore different topics, and gain a general
idea of a particular area of interest. Automatic document categorization [104, 105] is the
88
technique to help aggregate similar or related documents and present the resulting
clusters to the user in an intuitive and sensible way [106].
In recent years, various document categorization algorithms have been developed [105,
107-112]. Document categorization is based on the Cluster Hypothesis “closely
associated documents tend to be relevant to the same requests” [113]. These algorithms
can be divided into two categories. The first category depends on traditional machine
learning algorithms such as support vector machines, probabilistic classifiers, decision
trees, rule sets, instance-based classifiers, etc. The second category contains specialized
categorization algorithms for information retrieval, e.g. relevance feedback, linear
classifiers, generalized instance set classifiers. Document categorization is usually
implemented based on individual document attributes or inter-document similarities. In
all algorithms in both categories, key phrases are identified by systematically segmenting
and indexing documents. Arizona Noun Phraser [93] and Mutual Information [114] are
the two tools to extract key phrase.
The Document Categorization component is used to provide users with a list of
categories which are decided in the KCF so users can explore the results by categories as
well as by ranking orders in the Document List
5. Document Visualization.
Data visualization is to facilitate the access of large volume of data and reduce
information overload when a large number of search results are obtained. Card,
89
Mackinlay and Shneiderman defined data visualization as “The use of computersupported, interactive, visual representations of abstract data to amplify cognition” [115].
Applied in online search, visualization techniques help users identify the most relevant
documents from the entire set of retrieved documents. When the entire set of retrieved
documents is returned, it is represented in the visualization format. The user can get an
overview of the retrieved documents. Also the user can identify clusters of documents
which have similar features, trends in documents from different websites, and relations
among documents etc. Examples of such visualization techniques are the Jigsaw [116],
Geographic Information Systems (GIS) [117], Starfields [118] and Venn diagrams [119].
The Document Visualization component presents the results as a topic Self-Organizing
Maps (SOM) [120] that can be visualized as a 2-dementional neural network when the
classification process is applied. This function component is mainly implemented by
expanding the functions of 2-dementional information organizer for web documents [121]
based on SOM. The SOM is a good approach that automatically arranges high
dimensional statistical data so that similar documents are mapped close to each other.
3.5
Conclusions
In this chapter, we propose the concept of ASKE and our design of an ASKE framework
for information retrieval and analysis. We discuss the details of components in the
framework, including related research works, ideas behind each component, and
implementation details.
90
CHAPTER 4
SEARCHING TERRORIST GROUPS ON THE INTERNET
Terrorism threats have a wide range that spans personal, organizational, and societal
levels and have far-reaching economic, psychological, political, and social consequences
[122, 123]. Terrorists are using modern communication and information systems,
especially the Web, to their own advantage. The Web has evolved into a global platform
for people to use in disseminating and sharing ideas due to its easy access, anonymity,
and international character; however, terrorists are utilizing the Web for their relocation,
propaganda, recruitment, and communication purposes.
Nowadays, all active terrorist groups have established their presence on the Internet [124]
via websites or online bulletin boards. In daily life, many terrorists, extremist groups,
hate groups, and racial supremacy groups seize upon the worldwide practice of using the
Internet to improve communication and aid organization, allow members to coordinate
quickly with large numbers of followers, and provide a platform for propaganda and even
training manuals. The Web also allows terrorists to reach a wide audience of potential
donors and recruits who may be located over a large geographic area. For example, the
famous racial supremacy group, KKK, uses a Web site (http://www.americanknights.com)
to distribute their ideas. The international terrorist group, Islamic Jihad, also uses a Web
site (http://www.abrarway.com) to disseminate their ideologies, develop their strategic
intelligence, and attract potential group members. In addition, terrorists are exploiting
online public forums such as Yahoo’s eGroups, Usenet discussion forums, bulletin
91
boards, and chat rooms. We call this alternative side of the Web, which is used by
terrorists, extremist groups, and their supporters, the Dark Web.
Since the Dark Web datasets are generated and/or used by terrorists and their supporters,
they would be analyzed to enable better understanding and analysis of the terrorism
phenomena from the “terrorists’ point of view”. These contents provide snapshots of
terrorist activities, communications, ideologies, relationships, and
evolutionary
developments. However, traditional approaches to study terrorism groups are not
applicable to collecting and analyzing Dark Web information due to lacking of a lack of
advanced methodologies for data collection and mainly relying reliance on manual
analysis [125], the traditional approaches to study terrorism groups are not applicable to
collecting and analyzing Dark Web information.
There are several problems that are preventing effective and efficient discovery of Dark
Web intelligence. The first problem is mainly associated with information overload. The
amount of data available on the Web is often overwhelming and unmanageable to the
counterterrorism experts. Also, there are large and scattered volumes of terrorism-related
data available from diverse sources available to analyze terrorist threats and system
vulnerabilities [126], and counterterrorism experts are hindered from integrating these
diverse sources and obtaining a comprehensive picture. There are currently no advanced
or new methodologies to identify, model, and predict linkages among terrorists and their
supporters. Another problem is that data posted on the Web are not persistent and may be
misleading. The resources may suddenly emerge, frequently modify their formats, and
92
then swiftly disappear or, in many cases, seem to disappear by changing their URLs but
retaining much of the same content [124]. Thus there is an urgent need to preserve these
resources before they are forever lost. This last problem mainly involves language
barriers faced by counterterrorism experts when dealing with the multilingual contents of
terrorists’ emails from all over the world. Due to these problems, there is no general
methodology for collecting and analyzing Dark Web information. Even if Dark Web
datasets are collected, without analytical tools, raw data are still not useful for the experts
to produce more research of genuine explanatory and predictive value. Thus, developing
advanced techniques to support intelligent information archiving, searching and new
approaches to analyze and map terrorism knowledge domains is an urgent and
challenging problem.
In this chapter, we will describe how the ASKE framework is applied to this specific
domain to develop an experimental Web-based counterterrorism knowledge portal, called
the Dark Web Portal, to support the discovery and analysis of Dark Web information and
provide an intelligent, reliable, interactive, and convenient interface with for the
counterterrorism experts. The remainder of this chapter is organized as follows. Section
4.1 reviews existing terrorism research portals that provide terrorism-related information
to experts and researchers. Section 4.2 presents the research questions. In Section 4.3, we
report our experience implementing of the Dark Web Portal in details. Section 4.4
provides our concluding remarks and suggests the future research directions.
4.1 Literature Review
93
Organization
Acquisition/Collectio
n
1. Internet
Archive (IA)
1996 -. Spidering to
collect open access
HTML pages
2. AntiTerrorism
Coalition
(ACT)
3. Prism
(ICT, Israel)
2003 -. Has 448
terrorist web sites &
egroups
4. MEMRI
5. Site
Institute
6. Weimann
(Univ. Haifa,
Israel)
Vigilante
Community
7. Internet
Haganah
8. Simon
Wiesenthal
Center,
Snyder
Social
Action
Institute
9.
Johathanrgal
t (geocities)
2002 -. Limited # of
web sites. Project for
Research of Islamist
Movements, Reuven
Paz, Director
2003 -. Jihad &
Terrorism Studies
Project
2003 -. Manual
collection, Rita Katz,
Director
1998 -. Manual
collection
2001- . Spidering.
Has 100s links to web
sites. Confronting the
Global Jihad Project
2004 – tracking 4000
problematic websites,
and highlighting over
200 of these sites
Cataloging/Identificatio
n
Archive
Metadata such as URLs,
status, owner, data
created, collection
Research Center
Metadata such as URLs,
status, owner, data
created, ISP, group
affiliated
Storage
Preservatio
n
Access
Server
Ongoing
Via
project
web site
Database,
Server
Not
mentioned
Via
project
web site
Server
Not
mentioned
Via
project
web site
Not store
content
None
n/a
Server
Not
mentioned
Database
Ongoing
Via
project
web site
Closed
to public
Database,
Server
Not
mentioned
Via
project
web site
Not
mentione
d
Not
mentioned
Via
Compac
t Disks
““
““
““
““
““
2001 - spidering
Server
Stores back
Has 60-70 sites.
copies
Monitors sites that
closed.
Table 4.1 Current Approaches to Archive Terrorists’ Web Resources
Via
project
web site
94
In this section, we review current digital archiving for terrorists’ resources and existing
terrorism research portals provided by specialized research centers or vigilante
communities.
4.1.1
Digital Archiving for Terrorists’ Resources
Since the post September 11 war on terrorism has increased terrorist groups’ dependence
on the Internet, researchers, journalists, archivists, and vigilantes (including hackers) are
undertaking measures to ensure the continued but controlled availability of terrorists’
Websites for research and counterterrorism purposes. They are creating databases and
Websites for monitoring, collecting, classifying, preserving, and publicizing terrorists’
Websites. For example, the Jihad and Terrorism Studies Project by the Middle East
Media Research Institute (MEMRI) and the Project for Research of Islamist Movements
(PRISM) by the Interdisciplinary Center Herzilya, Israel, monitor Websites of militant
Islamic groups in their native languages and provide access to translated information and
metadata about the groups’ Websites and forums.
Table 4.1 lists the organizations involved in trying to preserve terrorists’ web-based
resources, which can be grouped into three categories: archive (1), research center (5),
and vigilante community (3). It uses Hodge’s digital preservation life cycle [127] to
summarize the activities of the organizations. The life cycle provides a framework for
managing digital preservation and includes six stages: creation, acquisition/collection,
cataloging/identification, storage, preservation, and access. Since we have limited
knowledge in identifying who authors a terrorist’ Website, the creation stage will not be
95
included because of the clandestine aspect of terrorist activities and anonymous nature of
Internet. Table 4.1 starts with acquisition and collection development, the stage in which
either the terrorist’s Website or information about the Website is acquired and collected.
Only three of the organizations as listed in Table 4.1, acquired terrorist’s’ Websites or
forums. The others only provide access to terrorists’ groups URLs and selected metadata
such as the name that the Website is registered under in the Whois directory, ISP, and
date created. Most of these organizations store digital copies of terrorists’ Websites. In
terms of preservation approaches, only Internet Archive describes how they preserve
Websites. Preservation of terrorists’ Websites and forums is at a nascent level.
4.1.2
Terrorism Research Portals
In analyzing terrorism phenomena, terrorism research portals provide services which help
researchers locate, collect, and analyze Dark Web data. There are already numerous
information portals provided by specialized research centers such as the Center for the
Study of Terrorism and Political Violence (CSTPV), located at St. Andrews University,
Scotland, and directed by noted terrorism researcher, Professor Paul Wilkinson and
formerly co-directed by Dr. Bruce Hoffman, Rand Corporation. These centers conduct
terrorism research and provide portals as a service for academics, journalists,
policymakers, students, and the general public. Terrorism research centers' portals are
primarily providing information retrieval and dissemination services except for a few
organizations such as the Terrorism Research Center (TRC) and the National Memorial
Institute for the Prevention of Terrorism (MIPT) that have expanded their functions to
96
include personalization (TRC) and the Emergency Responders Knowledge Base (MIPT).
For example, the TRC, founded in 1996, has the highest number of portal features
(31/61), including four terrorism databases, and is highly recommended with about 5,000
incoming links [128].
In Kennedy and Lum’s empirical study [123] of how criminal justice scholars can expand
their research to the terrorism domain, they generated lists of organizations conducting
terrorism research and 28 different datasets. Using their list of terrorism organizations
and Carnegie Mellon University’s portal taxonomies [129], the terrorism web portals
were examined to identify their features, types of information such as databases of
terrorists’ Websites, incident databases, and integrated applications that are available
[130].
For thirty of the 97 portals, error messages were received and new URLs could not be
identified. For thirteen other portals, terrorism information could not be located. This
may be associated with the dynamic nature of the Web and the fact that content is
constantly being removed from organization’s web servers. For the remaining 54
terrorism portals, we identified the types of information available and features of a
terrorism information portal using CMU’s informational dimensions such as collection,
application, and value-added applications, and the percentage of terrorism research
organizations providing the services. For the 54 terrorism portals, the information was
divided into two categories: unstructured and structured information. For unstructured
terrorism information, 74% were documents (full-text); 54% were links to external
97
resources; 48% were educational resources; 39% were news; and 28% were
Congressional testimonies. Structured information focused on terrorism incident data
(40%). None of the web sites provide access to or maintain information generated by
terrorists such as their Websites or forums.
Terrorism research centers' portals provide access to a diversity of unstructured (e.g.,
reports, news stories, transcripts) and structured (terrorism incident database) information
but fall short of analysis tools for integrating the resources and supporting information
fusion (including post-retrieval analysis). After searching the terrorism portals, returned
results are presented as lists of ranked URLs. The user has to manually browse through
the lists to locate relevant resources and establish relationships among the documents.
Since terrorist groups are from all over the world, the language barrier problem has to be
addressed to study the multilingual Dark Web data. Terrorism research centers’ portals
mainly focus on maintaining and providing access to terrorism incident database. The
collections being archived by these portals are often language specific, thus restricting
counterterrorism researchers to from obtaining a comprehensive understanding of Dark
Web information in different languages.
4.1.3
Multilingual Issues
Terrorism is an international issue and terrorism-related information is in various
European, Asian, and Middle Eastern languages. However, language barriers prevent
effective and efficient discovery of terrorism intelligence. The broad diversity of the
98
terrorism-related Websites presents a substantial research challenge in the field of
information retrieval.
Multilingual Information Retrieval (MLIR), responding to a query by searching for
documents in more than one language, has been studied to solve the problem. Most
reported approaches translate queries into the document language, and then perform
monolingual retrieval. There are three main approaches in query translation: using
machine translation, a parallel corpus, or a bilingual dictionary. The machine translationbased (MT-based) approach uses existing machine translation techniques to provide
automatic translation of queries. A corpus-based approach analyses large document
collections (parallel or comparable corpus) to construct a statistical translation model.
Performance relied largely on the quality of the corpus. Parallel corpus is very difficult to
obtain, especially for certain domains such as terrorism. In a dictionary-based approach,
queries are translated by looking up terms in a bilingual dictionary and using some or all
of the translated terms. This is the most popular approach because of the wide availability
of machine-readable dictionaries. Various techniques have been proposed to reduce the
ambiguity and errors introduced during query translation. Among these techniques,
phrasal translation, co-occurrence analysis, and query expansion are the most popular.
4.2 Research Questions
We will use the ASKE approach to design and develop a knowledge portal to address the
challenges in the counterterrorism domain. In this study, we aim to address the following
research questions:
99
1. How can intelligent collection building techniques proposed in ASKE framework be
used to build and maintain a high-quality, up-to-date collection of Dark Web data,
and help resolve the information management problems in counterterrorism research?
2. How can information searching, semantic search, text mining, post-retrieval analysis,
and Cross-Language Information Retrieval techniques help counterterrorism
researchers efficiently access, analyze, and understand the Dark Web collection?
3. How can artificial intelligence and visualization techniques be applied on the Dark
Web collection to enable better understanding of the terrorism phenomena and
support the knowledge creation and discovery patterns in counterterrorism research?
The remainder of the paper presents our work in studying these three questions.
4.3 Implementation of Dark Web Portal
To study the research questions above, we build an intelligent Web portal called Dark
Web Portal based on ASKE framework to assist counterterrorism experts to locate,
collect, access, analyze, and manage Dark Web data. Through the Dark Web Portal,
experts will be able to quickly locate specific Dark Web information in the data
collection through keyword search and semantic search. To cater to the nature of
terrorism phenomena and the requirements of counterterrorism experts, a key component
“the integration of multilingual information resources” is added to the ASKE framework.
4.3.1
Dark Web Data Collection Building
100
Since the post September 11 war on terrorism has increased terrorist groups' dependence
on the Internet and their "born digital" content is at-risk, it is imperative to assure longterm research access to multilingual terrorist digital information. As we discussed before,
neither manual selection method nor automatic Web crawling method is appropriate to
build a Dark Web archive with both high precision and high coverage.
Based on the approach proposed in ASKE, there are seven major steps in collecting
Terrorism Websites which were mainly done through systematic and careful semiautomatic work.
•
Identify terrorist groups from reliable sources: retrieve terrorist groups’ detail
information, such as terrorist group names, leaders, etc. from reliable sources, for
example, the terrorism reports of the governments of each country;
•
Identify terrorist group URLs and forum URLs: manually identify the URLs created
by the terrorist groups, e.g., using terrorist group names and terrorism keyword
lexicon in native language to search the general purpose search engines; adopt these
URLs as the starting URLs, and apply the method discussed in ASKE framework to
extract the favorite links, out-links, and back-links, then after filtering expand the
starting URLs; often the identified terrorist group Web sites contain forum sections or
provide links to their major forums.
•
Identify forums hosted on public ISPs: besides forums on extremists’ own Web sites,
many extremist forums are hosted on public ISP servers such as Yahoo! Groups and
Google Groups, AOL Groups, MSN Groups, and other regional ISPs. We identify a
101
list of these popular public ISPs and search for relevant forums using the terrorism
domain lexicon we created in the previous step; by browsing through the messages of
the forums returned from our search, relevant extremists’ forums can be extracted;
•
Filter the identified Web sites: The Web sites and forums identified from the previous
steps are filtered by domain experts to make sure that irrelevant or bogus sites do not
make way into our final collection. After the filtering, contents from the identified
Web sites are ready to be collected in the next step.
•
Collect Web content generated by terrorist groups: automatic Web crawler is applied
here to collect digital content generated by terrorist groups in all formats, including
Web page format, Text format, and Media format; the Web crawler also collects meta
data from forums, such as authors, headings, postings, threads, time-tags, etc.;
•
Generate multilingual Dark Web collection: to make the Web content collected in the
last step searchable for information seekers, each document is indexed into words,
and the relationships between the words and the documents are recorded; various
information retrieval techniques, such as stemming and stop-word removal, can be
applied if necessary; the resulting searchable indexes are then stored into a database
for document retrieval and further analysis;
•
Build semantic Dark Web collection: the semantic Dark Web collection is built to
support semantic search and analysis; the terrorism ontology is a way of organizing
data and establishing relationships between concepts; we uses an existing one on the
web at http://www.mindswap.org/dav/ontologies/terrorism.owl, which contains
seventy different classes, and 173 properties (71 DatatypeProperties and 102
102
ObjectProperties); the semantic collection is generated via the tool “Metadata
Extractor”.
To generate a comprehensive picture of Dark Web for counterterrorism researchers, our
Dark Web collection is complemented by collecting the information generated by
terrorist groups in different regions (Eastern Asia, United States, South America, and
Europe, etc.) and different languages (Arabic, English, Spanish, etc.). The process from
identifying group URLs to generating a multilingual/semantic Dark Web collection will
be repeated periodically to keep the collection up-to-date. Also, the information collected
during different time periods can be analyzed to study the dynamic evolution of the
terrorist groups over time. In Dark Web portal, we collect terrorist group Web sites and
U.S. domestic extremist forums separately. We will report the details of these collections
in the following sections.
1. Dark Web collection for Terrorist group Web sites
Our goal is to build a high-quality, up-to-date Dark Web collection which contains
multilingual information created by major terrorist groups in the world. To keep the Dark
Web collection up-to-date, we conducted two batches of Dark Web collection building in
April 2004 and June 2004.
In April 2004 we started the process by identifying the groups that are considered by
authoritative sources as terrorist groups. The main sources we used to identify US
domestic
extremist
groups
include:
http://www.adl.org/learn/ext_us/default.asp),
Anti-Defamation
League
(ADL,
FBI
103
(http://www.fbi.gov/congress/congress02/jarboe021202.htm
http://www.fbi.gov/congress/congress01/freeh051001.htm),
and
Southern
Poverty
Law
Center (SPLC, http://www.splcenter.org/intel/history.jsp), Militia Watchdog (MW,
http://www.militia-watchdog.org/m1.htm), and Google Web Directory (GD). To identify
international terrorist groups we relied on the following sources: United States
Committee For A Free Lebanon (USCFAFL), Counter-Terrorism Committee (CTC) of
the UN Security Council (UN), US State Department report (US), Official Journal of the
European Union (EU), and government reports from the United Kingdom (UK),
Australia (AUS), Japan (JPN), and P. R. China (CHN). These sources have been
identified following the recommendations of core terrorism authors.
A total of 224 US domestic extremist groups (see Appendix A) and 440 international
terrorist groups (see Appendix B) have been identified. Based on the categorization
method suggested by SPLC, the US domestic groups were categorized into 7 different
categories: Black Separatist (BS), Christian Identity (CI), Militia, Neo-Nazis (NN), NeoConfederate (NC), Racist Skinhead (RS), White Supremacy (WS), and others. Figure 4.1
summarizes the number of US domestic groups we identified from each source within
each category. From the figure, we can see that the collection covers a large variety of
US domestic extremist groups and could serve as a good resource to study all aspects of
terrorism in the US.
104
Figure 4.1 Number of U.S. domestic groups identified from each source within each category
Figure 4.2 Number of international groups identified from each source within each geographical location
105
For international terrorist groups, there is no standard categorization available. The
domain expert identified the categories based on literature and her knowledge. In addition,
we categorized the international groups based on their primary geographical locations.
Figure 4.2 summarizes the number of international groups we identified from each source
within each geographical location. From the figure, we can see that the collection covers
terrorist groups from a large span of geographical locations in the world and could serve
as a good collection to study cross-jurisdiction terrorism phenomena.
The spider agents are deployed as sensors in the architecture of social sensor networks.
Figure 4.3 depicts the structure of the sensor network for U.S. domestic extremist groups.
The sensor network consists of two different levels of networks: the websites and
categories that have higher activity levels are located in the inner circle (Level 1), e.g.,
“White Supremacy” category; the outer circle (Level 2) is composed the related websites
and groups which are extended from the inner circle based on link connections and
organization connections. With the sensor network, it becomes possible to sense dynamic
nature of U.S. domestic extremist activities in a timely manner. The positions of websites
and extremist groups in the structure are dynamically changed as spider agents sense
various activities in forums, websites, and groups.
106
Figure 4.3 The social sensor network for U.S. Domestic Extremist Groups
107
US Domestic
Spanish
Arabic
Category
# of pages
Black Separatist
121
Christian Identity
Militia
36,366
15,997
Neo Confederate
2,478
Neo Nazis
Racist Skinhead
19,384
10
White Supremacy
12,249
Others
Left Wing Paramilitary Group
7,692
45
Leftist Group
2
Maoist Rebel Group
Marxist Insurgency/Guerrilla
3,704
540
Separatist Group
35,497
Socialist Group
Others
56,671
0
Jewish
1,498
Secular
Shi'a Muslim
1,062
916
Communist/Socialist
1,291
Secular
6,185
Sunni Muslim
Others
252,177
6,495
Table 4.2 Number of pages collected for the terrorist groups within each category
For the first batch, we manually identified the URLs of the terrorist groups’ web sites
from the reports alluded to by the sources mentioned above then searched the web using
the group names in their native language as queries. To ensure that our collection covers
all the major regions in the world, we sought the assistance of language experts in
English, Arabic, Spanish, Japanese, and Chinese to help us collect URLs in different
regions. At this time, we identified 114 URLs from US domestic extremist groups, 48
URLs from Spanish-speaking terrorist group, and 66 URLs from Arabic-speaking groups.
After the URL of a group is identified, we used the SpidersRUs toolkit
(http://ai.bpa.arizona.edu/spidersrus), a multilingual Digital Library building tool, to
108
collect all the static text-based Web pages (html, txt, pdf, and doc) under that URL. So far,
we have collected 500,000 Web pages created by US domestic groups, 300,000 Web
pages created by Arabic-speaking groups, and 100,000 Web pages created by Spanishspeaking groups. Table 4.2 demonstrates the number of Web pages for the terrorist
groups within each category.
US Domestic Collection
# of Files
Volume (Bytes)
Total
396,105
27,630,493,405
Indexable Files
316,692
9,288,983,289
HTML Files
102,168
3,761,303,743
Word Files
425
301,294,350
PDF Files
1,201
947,561,966
Dynamic Files
207,985
2,959,621,213
Text Files
4,112
793,433,495
Excel Files
2
140,800
PowerPoint Files
7
1,035,738
XML Files
792
524,591,984
Multimedia Files
70,832
15,478,613,165
Image Files
65,296
1,214,302,446
Audio Files
4,898
11,647,742,758
Video Files
638
2,616,567,961
Archive Files
767
409,018,557
Non-Standard Files
7,814
2,453,878,394
Table 4.3 Documents spidered in the second batch for US Domestic Extremist Groups
In June 2004 the second batch collection was built by expanding and updating the first
batch collection. More authoritative sources are adopted to identify terrorist groups, such
as a report from Dartmouth College (https://www.ists.dartmouth.edu/TAG/cybercapabilities-terrorist.htm). Through automatically extracting out-going URLs from
favorite links of the terrorist groups’ URLs that we have identified in the first batch and
manually filtering, 386 US domestic, 151 Arabic, and 83 Spanish terrorist URLs were
included in the second batch collection. All types of documents were collected in the
second batch collection. As well as the static text-based files (html, txt, pdf, and doc)
109
included in the initial collection, the second batch collection also includes: Dynamic textbased files (asp, php, cgi, etc), Media files (images, video and audio files), Archive files
(zip packages, rar packages, etc), and Non-standard file types. Tables 4.3-4.5 present the
detail statistics of the Web documents spidered for US domestic extremist groups,
Arabic-speaking terrorist groups, and Spanish-speaking terrorist groups respectively.
Table 4.6 summarizes the comparison between the 1st and 2nd batch collection.
Arabic Collection
# of Files
Volume (Bytes)
Total
222,687
12,362,050,865
Indexable Files
179,223
4,854,971,043
HTML Files
44,334
1,137,725,685
Word Files
278
16,371,586
PDF Files
3,145
542,061,545
Dynamic Files
130,972
3,106,537,495
Text Files
390
45,982,886
PowerPoint Files
6
6,087,168
XML Files
98
204,678
Multimedia Files
35,164
5,915,442,276
Image Files
31,691
525,986,847
Audio Files
1,973
3,750,390,404
Video Files
733
1,230,046,468
Archive Files
1,281
483,138,149
Non-Standard Files
7,019
1,108,499,397
Table 4.4 Documents spidered in the second batch for Arabic-Speaking Terrorist Groups
Spanish Collection
Total
Indexable Files
HTML Files
Word Files
PDF Files
Dynamic Files
Text Files
Excel Files
PowerPoint Files
XML Files
Multimedia Files
Image Files
Audio Files
# of Files
332,134
281,382
154,671
389
156
125,586
556
2
7
15
44,671
44,284
316
Volume (Bytes)
6,207,859,955
4,421,338,803
2,505,598,128
21,549,415
34,793,876
1,846,283,672
9,829,425
71,168
29,34,272
2,78,847
1,6098,93,751
9696,82,787
5715,19,457
110
Video Files
71
686,91,507
Archive Files
110
229,34,999
Non-Standard Files
5,971
1536,92,402
Table 4.5 Documents spidered in the second batch for Spanish-Speaking Terrorist Groups
Seed
URLs
Total
From
Literature &
Reports
From Metasearch
From
outlink
extraction
Terrorist Groups
Collection
Size
Total
US Domestic
Spanish
Initial
Initial
81
2nd
Batch
386
63
Arabic
Initial
37
2nd
Batch
83
69
2nd
Batch
128
266
0
0
23
31
0
0
37
48
46
66
18
120
0
32
0
31
94
219
7
10
34
36
125,610
396,105
106,459
222,687
322,524
332,134
0
70,832
0
35,164
0
Multimedia
Files
Table 4.6 Comparison between the 1st and 2nd batch collection
44,671
2. Dark Web collection for US domestic extremist forums
Following the ASKE framework, we started our forum collection from identified 386 U.S.
domestic extremist groups URLs. Using the information of these groups as queries, we
searched major search engines and public ISP Web sites (e.g., Yahoo! Groups, Google
Groups, etc.) for forums created and maintained by the identified extremist groups. After
the expansion and filter steps, we identified a total of 105 extremist forums of which 12
are hosted on stand-alone extremist Web sites, 47 are hosted on Google Groups, 31 are
hosted Yahoo! Groups, ten are hosted on MSN Groups, and five are hosted on AOL
groups (see Appendix C). Figure 4.4 provides a summary and categorization (based on
SPLC) of the forums we identified.
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As shown in Figure 4.4, the identified forums were created by a wide variety of U.S.
extremist groups and should serve a comprehensive resource for domestic extremist
movement studies.
35
30
25
20
15
10
Neo-Nazis
White Supremacist
Black Separatist
5
Christian Identity
0
Militia
Neo-Confederate
Others
Figure 4.4 Summary and categorization of identified U.S. domestic extremist forums
After obtaining membership for the password-protected forums, we spidered data from
the identified extremist forums. Table 4.7 is a summary of the number and volume of
different types of documents we downloaded from the extremist forums.
As we can see from Table 4.7, textual files (e.g., HTML files, PDF files, Word Files,
Excel Files, etc.) are the largest category on extremist forums. Multimedia files also
comprise a significant presence on extremist forums. We found that the usage pattern of
multimedia on standalone extremist forums is quite different from that on extremist
forums hosted on pubic ISP servers. The average number of multimedia files posted on
standalone extremist forums (1195.4) is much larger than that of the public ISP hosted
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extremist forums (70.8). However, even though a smaller number of image files are
posted on the public ISP hosted forums, they are much larger in volume. For audio and
video files, the ones that were posted on standalone forums are larger both in terms of
quantity and volume. The different levels of multimedia usage in the two types of forums
could be resulted from the limitations on multimedia use that the public ISPs usually set
in their forums.
Stand Alone Forums
Public ISP Forums
# of
Files
# of Files
Volume
(Bytes)
Volume
(Bytes)
116,419
7.7G
524,652
20G
Textual
Files
93,655
6.5G
350,046
10.7G
Multimedia
Files
21,518
1.1G
6,511
1.3G
Image
Files
21,177
374M
5,393
1.06G
Audio
Files
107
405M
589
186M
Video
Files
234
358M
529
21M
NonStandard
Files
1,246
45M
168,095
9G
Total
Table 4.7 Summary of Document Types in the Forum Collection
Another difference between the two types of forums is the use of non-standard files. We
found the amount of non-standard files on the public ISP forums was much larger than
the stand alone forums. These non-standard files could be files that can only be opened
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by using some obsolete software or they can be encrypted materials that were deliberately
made inaccessible for normal software.
As described in ASKE framework, our spider agents automatically extract important
information such as number of participants and postings from the forum files. Figure 4.5
gives an example of information that has been extracted, such as userid, user rank,
posting time, thread title, posting message, reference message, etc. This information can
be used in various trend analysis and temporal analysis in the future.
We found that the domestic extremist forums are very popular. In our collection, the
average number of participants of each forum is 788.5 and the average number of posts
on each forum is 6461.6. The largest forum in the collection is the Neo Nazi forum
StormFront.org with 55,834 registered participants, 189,816 existing threads, and
1,923,169 postings. The most popular category is Neo Nazi forums with average numbers
of 2234.4 participants and 21,217 posts per forum. The second most popular category is
the White Supremacy forums with numbers of 80.31 participants and 336 postings per
forum. The smallest category is the Eco Terrorism forums with, on average, 15
participants and 147.6 postings per forum.
Through statistical analysis, we found that the numbers of participants and postings on
the extremist forums follow a power-law distribution:
p ( n) ~ n − γ
where n is the number of participants or postings on a forum; and p(n) is the probability
of a forum having n participants or postings.
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Figure 4.5 An example of U.S. domestic extremist forums
Regression analysis showed (see Figure 4.6) that the number of participants on extremist
forums follows a power-law distribution (R = 0.92) with exponent g = 0.339 and the
number of postings on extremist forums follows a power-distribution (R = 0.78) with
exponent g = 0.210. Such distributions indicate that “preferential attachment effect” may
have affected the development of extremist forums. The more popular forums tend to
115
attract more participants and become more and more popular; while the less popular
forums are likely to remain unpopular. Thus, monitoring the most popular extremist
forums is very important since those forums are where most future participants go and
where ideas are expressed and contacts made.
Figure 4.6 The distributions of number of participants and number of postings on extremist Forums
4.3.2
Post-retrieval Analysis and Multilingual Support
To address the information overload, the Dark Web Portal is fitted with post-retrieval
components, including categorizer, summarizer, and visualizer.
Document Summarizer. Automatic summarization has been applied as a document
preview tool in many information retrieval systems [117]. In the Dark Web Portal, a
multilingual Summarizer was developed based on the AI Lab TXTRACTOR [102] that
uses sentence-selection heuristics to rank text segments. This heuristic strives to reduce
redundancy of information in a query-based summary [131]. It supports summarizing
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Web documents in Arabic, English, and Spanish at the same time. The summarization
involves three components: 1) sentence evaluation, 2) segmentation or topic boundary
identification, and 3) segment ranking and extraction. First, a sentence evaluation
component parses the original Web page and extracts all sentences. These sentences are
evaluated based on linguistic heuristics including presence of cue phrases (e.g. "in
summary", "therefore" in respective languages), tf*idf score normalized for the sentence
length, sentence position, and sentence length. Second, the Text-Tiling algorithm [132] is
used to analyze the Web page and determine where the topic boundaries are located. The
Web page is thus segmented into its main topics. A Jaccard similarity function is used to
compare the similarity of different blocks of sentences. Third, the Summarizer ranks the
document segments based on the scores given to the sentences and extracts high-ranking
sentences from different segments as summary sentences.
Document Categorizer. The document categorizer organizes the returned Web
documents into 20 or fewer different folders labeled by the key phrases appearing most
frequently in the page summaries or titles. Key phrases with high occurrences in the
returned results are extracted as folder topics. Web documents that contain a folder topic
are included in that folder. One Web document may appear in several folders if it
contains multiple folder topics.
For Web documents in Arabic, when the categorizer is invoked all the returned results are
processed and key phrases that appear in the titles and summaries are extracted by
matching to a phrase lexicon in the respective language. To create the lexicons, we
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extracted meaningful phrases from the Dark Web collection by using the Mutual
Information program, which is based on a statistical method [114]. The method is an
iterative process of identifying significant lexical patterns by examining the frequencies
of word co-occurrences in a large amount of text.
For Web documents in English or Spanish, we use the Arizona Noun Phraser (AZNP) [93]
to extract meaningful phrases from the titles and summaries of the search results.
Developed at the Artificial Intelligence Lab of the University of Arizona, AZNP extracts
all the noun phrases from each Web page automatically based on part-of-speech tagging
and linguistic rules. An indexing program calculates the frequency of occurrence of these
phrases and selects the 20 most frequently occurring phrases to index the results.
In our document categorizer we are using only titles and summaries to extract keywords
since it is practical and permits dynamic categorization. Previous research has shown that
clustering based on snippets is almost as effective as clustering based on a whole
document [113].
Document Visualizer. The Dark Web Portal also supports visualizing the retrieved Web
documents and helps reduce information overload when a large number of search results
are returned. Using the document visualizer, counterterrorism researchers can obtain a
meaningful and comprehensive picture of a large number of search results. The document
visualizer provides two types of visualizations: the Jigsaw and Geographic Information
Systems (GIS).
118
The Jigsaw is a two-dimensional map generated by using the Kohonen self-organizing
map (SOM) algorithm. SOM is a two-layered neural network that automatically learns
from the input Web documents and clusters them into different naturally occurring
groups, and it has been used in image processing and pattern recognition applications. In
the Jigsaw map, similar documents are assigned to adjacent regions which are labeled by
key phrases identified by the AZNP or the mutual information program. The size of a
region on the map indicates how many pages are assigned to it.
In the GIS SOM visualizer, Web documents are shown as points on a two-dimensional
map with their positions determined by the SOM algorithm. The map's background
shows contour lines representing the varying values selected by users (e.g., frequency of
occurrence of query terms in the Web pages) and is independent of the points' positions.
Users can navigate on the map by clicking on the buttons and resize a certain part of the
map by dragging a rectangle that will highlight the set of Web pages listed on the bottom
right side of the pop-up window.
Multilingual Support. To support multilingual retrieval of terrorism-related Websites,
we proposed to use MLIR and Machine Translation in our Dark Web Portal. A complete
Web-based multilingual retrieval system consists of five components: (1) Spiders (a.k.a.
Crawlers) to retrieve Web pages by recursively following URL links, (2) an Indexer to
tokenize multilingual Web pages into words or phrases, (3) a Query Translation Engine
to translate the query into document languages, (4) a Retrieval Engine to get relevant
119
results, and (5) a Document Translation Engine to translate retrieved documents into
users’ familiar languages.
Spiders are used to build multilingual collections. Indexers are designed to work with
different language encodings. For languages with a rich morphology such as Arabic,
language normalization is frequently used in indexing to improve retrieval performance.
The Translation component is the core of the system. It is responsible for translating
search queries in the source language into the target language. We used a dictionarybased approach combined with phrasal translation and co-occurrence analysis for
translation disambiguation. In the dictionary lookup process, the entry with the smallest
number of translations will be preferred over other candidates. In addition we conducted
maximum phrase matching. Translations containing more continuous key words will be
ranked higher than those containing discontinuous key words. Co-occurrence analysis
also was used to help choose the best translation among candidates. The Retrieval Engine
would be similar to those monolingual system retrieval engines since queries are
translated into document languages in the previous step. To make such a system useful to
end users, a document translation engine is necessary. Commercial machine translation
software such as SYSTRAN would serve our needs. With these five components, users
are able to retrieve documents in languages other than the query language and understand
the content of these documents.
4.3.3
Searching and Browsing in the Dark Web Portal
120
The Dark Web Portal supports searching and browsing terrorist groups in different
regions and languages, for example, English, Arabic and Spanish. In particular, the portal
provides three versions of user interfaces to accommodate users with terrorist groups’
native languages so that users can get better understanding of the terrorist groups. They
look similar and present the same functionalities, except that they use different encoding
schemes and languages.
By configuring KFC, users can easily get reports about the interested terrorism
information regularly. The following is a KFC example that supports to retrieve dynamic
information of an US extremist group “Women for Aryan Unity”.
<profile>
<salutation>Dr.</salutation>
<first_name>John</first_name>
<last_name>Casey</last_name>
<email>[email protected]</email>
<specialty>KKK, white supremacy</specialty>
</profile>
<favorites>
<favorite topic="" period="24h" type="URL">http://wau14.com/</favorite>
<favorite topic="" period="10m" type="URL">http://www.stormfront.org/forum/</favorite>
<favorite topic="" period="1h" type="URL">http://www.w-a-uargentina.blogspot.com/</favorite>
<favorite topic="" period="240h"
type="URL">http://www.stormfront.org/crusader/texts/wau/</favorite>
</ favorites>
<topics>
<topic id= "1">
<name>White Supremacy</name>
<description>white people are superior to people of other racial backgrounds</
description>
<keyword>white supermacy</keyword>
<keyword>superior</keyword>
<keyword>anti-black</keyword>
<keyword>Racism</keyword>
<keyword>Ku Klux Klan</keyword>
</topic>
<topic id= "2" parent_id= "1">
<name>Women for Aryan Unity</ name>
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<description>reveal new ways of thinking and new manners of acting that will
rediscover the mysteries of the race</description>
<keyword>Women for Aryan Unity</keyword>
<keyword>abortion</keyword>
<keyword>white children</keyword>
<keyword>compete with men</keyword>
</topic>
</topics>
<queries>
<query id="1">
<schedule>
<minute>0</minute>
<hour>8</hour>
<day_of_month>*</day_of_month>
<month>*</month>
<day_of_week>*</day_of_week>
</schedule>
<keyword order="1" type="">fund</keyword>
<keyword order="2" type="">activity or parade or magazine</keyword>
<keyword order="3" type="">schedule</keyword>
</query>
</queries>
In this KCF, the user “John Casey” would like to make sure 4 URLs are collected and
indexed,
and
scan
these
sites
in
different
frequency.
The
URL
http://www.stormfront.org/forum/ is an online forum, which will be scanned every 10
minutes. The main website http://wau14.com for the group “Women for Aryan Unity” is
checked every day. In the topic section, a category “White Supremacy” is defined, and
also the group is defined as a subtopic of the category. In the query section, John tries to
get the information of the group about fund raising, different activities, and schedules of
these activities. The report with search results related to the topics and defined by this
query will be delivered 8am every day.
In the Dark Web Portal, there are two types of search forms available: simple search and
advanced search. The default one is simple search. Users may switch between the two
using the “Advanced Search” link. For the simple mode (see Figures 4.7.a, 4.8.a), all
122
users need to do is enter one or multiple keywords into the search box, then the portal
will find the Web documents which include all of the keywords. For the advanced mode
(see Figures 4.7.b, 4.8.b), the advanced search screen will give users a much greater
range of options to choose from to refine the search. In addition to searching Web
documents with all of the keywords, users are able to find corresponding Web pages with
the exact phrase, or with at least one of the keywords, or without the keywords. There are
three additional ways of searching terrorist groups’ information. Users can either restrict
the results t within some terrorist categories as we discussed above, or choose different
file types (pdf files, Word files). Furthermore, a user can select the time period he/she is
interested in. Users may refine the search query with the individual or combined options.
After searching, the top 100 results are returned. On the first result page (see Figure 4.7.c,
4.8.c), the top 20 results are displayed in sorted order of relevancy to the query as
measured by tf*idf score. Users can check other results by using the “Next” link at the
bottom of the page. There are also search boxes available at the top of each result page to
allow users to carry out a quick search. For each query, the first result page displays
“Suggested Keywords”, a set of relevant keywords such that the user can expand or
refine the original search query, which are obtained via a Concept Space approach [133135]. Additionally users can switch among Webpage, pdf, and Word results by the
corresponding format links.
For each result, since these terrorist group Web pages often disappear randomly, cached
pages for different time periods are provided. We have built two batches of our Dark
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Web collection. The system also displays the terrorist group name, the corresponding
category, and the country where the terrorist group is located for each Web page. By
clicking on the “Sort by Group”, “Sort by Category”, and “Sort by Country” links, users
can sort the results by groups, categories, and countries very conveniently and easily
locate the terrorism information they need. The summarizer function is also implemented
for each result. Users can summarize each result flexibly using three or five sentences by
selecting the number of sentences under the result. The summarization result is shown in
Figures 4.7.d and 4.8.d.
By clicking on the “Organize” tab, users can go to the categorizer page where all the
results are categorized into folders with extracted topics. Clicking on the folders of
interest gives a list of URL titles that appear under the relevant folder for browsing (see
Figures 4.7.e and 4.8.e). To visualize the returned results, two visualizers are provided.
When the “Map” tab is clicked, a new window containing an SOM map is activated
(shown in Figures 4.7.f and 4.8.f). Users can click on a region to see a list of the pages on
the right and can open the pages by clicking on the titles. The GIS map can be activated
by clicking on “Try our new visualization tool” (see Figures 4.7.g). Users can navigate on
the map by clicking on the buttons and resize a certain part of the map by dragging a
rectangle that will highlight the set of Web pages listed on the bottom right side of the
pop-up window.
In addition, the portal provides Web directories for US domestic groups, Arabic-speaking
terrorist groups, and Spanish-speaking terrorist groups (Figures 4.7.h and 4.8.h). These
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Web directories are organized in a hierarchical structure and compiled based on the
categorization method suggested by SPLC and our domain expert’s knowledge. By
traversing the predefined Web directory hierarchies, users may easily locate the terrorist
groups or their supporters’ websites.
4.3.4
Multilingual Support
To support multilingual retrieval of terrorism-related Websites, the Dark Web portal
helps English-speaking experts access, analyze, and understand Dark Web information in
Arabic languages.
Arabic is a morphologically rich language. In order to handle the variations in the way
text can be represented in Arabic, we performed normalization on Arabic Web pages
including stemming and stopword removal. In stemming, Arabic terms were
transliterated into Roman characters. Prefixes and suffixes on the transliterated terms
were removed using a heuristics-based approach. Removed prefixes are :‫ ا‬, ‫ ي‬, ‫ ل‬, ‫ م‬, ‫ ة‬, ‫ ال‬,
and suffixes are: , , ,
,‫ ه‬, ‫ ه‬,‫ ه‬, ‫ آ‬,
,
, , ‫ و‬, ‫ ن‬, ‫ و‬, , ‫وا‬. The stemmed
transliteration was converted back to Arabic. Since most Arabic Web pages often omit
diacritics (weak vowels) and only preserve letters of alphabets, diacritics were removed
to ensure consistency. A stopword list of 347 words was originally taken from the
University of Neufchatel’s (http://www.unine.ch/info/clef/) repository. One hundred
more words were manually added from the training process of Mutual Information and
our final stopword list contains 550 words.
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We used the dictionary-based approach in query translation. Two dictionaries were used
at this stage: an English-Arabic dictionary constructed from Al-Misbar, an online
dictionary (more than 20,000 entries), and an Arabic-English dictionary from Tuft’s
University (50,000 entries). We obtained more than 60,000 entries when combining two
dictionaries and diacritics were removed in dictionary entries. The collection was first
indexed against the combined dictionary. Co-occurrence scores between every two
dictionary terms were calculated and stored in the database. In order to do query
expansion, we integrated the Arabic Stemmer with the Mutual Information program to
extract meaningful Arabic phrases. After training, we extracted 20,383 entries (Arabic
phrases) from the collection in Arabic. These phrases were used as potential query
expansion phrases. Pseudo relevance feedback was used to perform query expansion. Our
retrieval model is a standard tf*idf based model.
Figure 4.9 shows a sample user session of the Dark Web Portal. After the user provides
an English query (Figure 4.9.a) the system will first apply a word-by-word translation
which shows all the possible translations of a word (Figure 4.9.b). In the phrasal
translation phase, the system detects multiple English words as a phrase, based on a
phrase dictionary, and translates them into one Arabic word/phrase (Figure 4.9.c). In Cooccurrence Analysis, the system determines the best combination of possible translations
based on their co-occurrence in a domain-specific Arabic document corpus. The best
translation combination is highlighted among all the possible translations (Figure 4.9.d).
The best translation combination is used to retrieve Arabic documents and the results are
displayed in Figure 4.9.e. In the last step, retrieved Arabic documents are translated back
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to English, the original query language, using the machine translation system (Figure
4.9.f).
4.3.5
Semantic Search in the Dark Web
Although the semantic search engine is still in its infancy in Dark Web Portal as the
ranking algorithm has not yet been fully investigated, it produces encouraging results.
Figure 4.10 shows the results when we search the famous terrorist group “Roubaix Gang”
using semantic search engine. Behind the scene, the collections are annotated by the
metadata extractor and thus being associated with semantic mark-ups.
As shown in the figure, the document which mentions the group name “Roubaix Gang”
and its related activities appears in the top, as it gets the biggest relation weight. The
documents that mention other semantic entities (e.g. members of “Roubaix Gang”) with
which many terrorist members have relations also get good rankings, since in the ASKE
framework we give implicit relations half the weight of the explicit ones. With this
profound semantic search mechanism, we can make use of the available semantic
relations between different resources to bring forward most relevant information.
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a. Simple Search Page
c. First Result Page
b. Advanced Search Page
d. Web page
Summarization Results
f. Visualizer - SOM
e. Web Page Categorization
Results
h. Web Directory
g. Visualizer - GIS
Figure 4.7 The screenshots of Dark Web Portal for U.S. domestic groups
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a. Simple Search Page
b. Advanced Search Page
c. First Result Page
d. Web page
Summarization Results
e. Web Page Categorization
Results
f. Visualizer - SOM
h. Web Directory
Figure 4.8 The screenshots of Dark Web Portal for Arabic-speaking terrorist groups
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a. Search Page: User types
in English Query
b. Word-by-word Translation:
System lists all possible
translations
c. Phrasal Translation: The
system detects and translates
multi-word concepts in the original
query as phrases
d. Co-occurrence Analysis: The
system determines the best
combination of possible translations
based on their co-occurrence in an
domain-specific Arabic document
corpus.
e. The system retrieves:
Arabic documents using the
translated query and
provides English translations
of the titles and summaries of
the results.
f. The system provides
English translations of the
titles and summaries of the
results using a machine
translation system.
Figure 4.9 Jihad multilingual portal user sessions
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Explicitly
mention
Ranking
Members of
Roubaix Gang
Figure 4.10 The screenshot of semantic search for the keywords "Roubaix Gang"
4.4 Conclusions and Future Directions
Information overload, uncertain data quality, and lack of access to integrated datasets and
advanced methodologies for studying terrorism are major hurdles and challenges which
both traditional and new counterterrorism researchers have to overcome. In this chapter
we reported the current status of digital archiving for terrorists’ resources, reviewed
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terrorism research portals by specialized research centers, and analyzed contemporary
technology solutions to help fill in existing gaps. By using the ASKE framework, we
have proposed an approach to build an intelligent Web portal, called Dark Web Portal, to
assist counterterrorism experts to locate, collect, access, analyze, and manage Dark Web
data.
We have discussed the development of Dark Web Portal, which is aimed at supporting
the counterterrorism research community. Two batches of collections which cover US
domestic groups, Arabic-speaking terrorist groups, and Spanish-speaking terrorist groups
have been implemented. We believe that it will facilitate better understanding of the
global terrorism phenomenon and provide a systematically integrated research tool for
culturally diverse counterterrorism researchers.
In this chapter, we confirm that ASKE framework is an applicable and efficient approach
to build domain/application specific web portals. The semantic search facility developed
within the framework takes advantage of semantic representation of information to
facilitate keyword searching. It produces encouraging results. More further investigation
need to be conducted: i) a more fine-grained ranking algorithm which gives appropriate
consideration for all the factors affecting the search results, and ii) the effect of implicit
relations on search results.
During the process of building Dark Web portal, we are aware of a number of limitations
associated with this framework. For example, the manual specification of mappings in the
process of setting up the metadata extractor makes the approach heavy to launch.
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CHAPTER 5
MONITOR FILE SHARING IN P2P WORLD
In 2006, the Motion Picture Association of America claimed that the industry was losing
$6.1 billion annually in global wholesale revenue because of the impact of pirated DVD
movies and Internet downloads, based on the study conducted by LEK Consulting LLC
[136]. Although we have no details about how the estimates are calculated, it is apparent
that movie piracy has kept increased recently and people are sometimes happy to get
movies for free even by violating intellectual property rights. The similar situation
happens in music industry, publication industry, and software industry also.
Among six types of common piracy ways (Optical disc piracy, Videocassette piracy,
Internet piracy, Signal theft, Broadcast piracy, and Public performance), the Internet
piracy becomes more and more popular as the growth of Peer-to-Peer (P2P) file sharing
has been surprisingly fast. It is estimated that currently up to 90 percent of local and 60
percent of backbone traffic is P2P traffic [137]. The financial losses to copyright owners
due to P2P can be more severe and cannot be reduced in a short period, as with P2P
techniques, it is much easier to distribute copyrighted materials over the Internet in digital
forms. Currently there is no efficient way to prevent distribution over P2P networks for
copyright holders. Sometimes, pirated materials are distributed over P2P networks even
before they are official released. For instance, the Telesync (TS) version of movies is
easily produced in the theatres with a cheap camcorder. To develop an effective anti-
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piracy strategy to fight piracy over P2P networks, we have to understand the details of
how pirated materials are generated and disseminated in P2P world.
In this chapter, we will describe how to build an anti-piracy system based on the ASKE
framework, generating insights of P2P download activities. The remainder of this chapter
is structured as follows: Section 5.1 presents the literature review; Section 5.2 proposes a
feasible approach to implement the components in ASKE for different P2P protocols,
including the challenges and problems during the process we implement them; we report
the services provided to copyright owners and present a case study in Section 5.3 and the
conclusions are given in Section 5.4.
5.1 Literature Review
In this section, we review P2P history, popular P2P networks, and the related research in
P2P domain.
5.1.1
P2P History
Since Napster started its service in 1999, P2P file sharing has grown to the point of
becoming a significant and growing component of Internet traffic [54, 59].
P2P networks are the virtual networks of computers, in which there are only peers,
instead of servers and clients in the traditional networks. Every computer or machine is a
peer with the same functionality, although they may have big differences with respect to
hardware conditions, operating systems, or connection speed, etc. In traditional networks,
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each node has its own operations, and the servers are definitely authorities. In contrast, in
P2P networks, there is no centralized authority node, and every peer shares the same
configuration and connects to each other equally at the application level. As the
exception, some P2P networks have some special peers (super nodes) that are used to
handle keyword queries.
Although P2P networks became popular only for recent years, the concept of P2P
networking emerged in the early period of network systems. Surprisingly, we can treat
both ARPANET in the late 60’s and Usenet in the late 80’s as the predecessors of today’s
P2P networks, since they have the similar features of P2P networks, such as distributed,
decentralized networks and used for sharing files among equal peers. The use and
development of P2P networks is neglected as World Wide Web growed dramatically in
the early 90’s. However, a series of new technologies lead to the explosion of P2P
applications. First, the popular mp3 (the MPEG-1 Audio Layer-3) encoding [138] which
implemented huge data compression, with free mp3 players (e.g., winamp [139]).
Encodings that made considerable reduction for video data possible were also developed
after that (e.g., DivX [140], Xvid [141]). Second, high-speed Internet access becomes
popular to end users. Third, the Napster network [142] was started in 1999 totally, totally
changing the way of file sharing.
Since Napster’s service was forbidden to stop, many different types of P2P networks
have been developed. These new P2P networks support millions of users and billions of
file transfers. P2P applications have grown to be a considerable and dominant fraction of
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Internet traffic. In 2000, three popular P2P applications Gnutella, eDonkey2000, and
Freenet were released. As the first decentralized file sharing network, Gnutella considers
all connecting peers completely equal. Any peer fails would not affect the network.
eDonkey2000 provides client and server software, and server software is used to facilitate
keyword search functionality. Freenet was the first anonymity network. In 2001, Kazaa
and Poisoned for the Mac was released. Its FastTrack network was distributed, though
unlike Gnutella, it assigned more traffic to 'supernodes' to increase routing efficiency. In
July 2001, the LimeWire client and BitTorrent protocol were released. Until its decline in
2004, Kazaa was the most popular file sharing program. From 2002 through 2003, a
number of popular BitTorrent services were established, including Suprnova.org, isoHunt,
TorrentSpy, and The Pirate Bay. With the shutdown of eDonkey in 2005, eMule became
the dominant client of the eDonkey network. Currently the most popular networks are
BitTorrent via uTorrent, Gnutella via Limewire, and the eDonkey network via eMule.
Most P2P file sharing applications are implemented satisfying the following requirements:
•
Adaptivity: P2P applications run in dynamic P2P networks, where peers may join
or leave the network unpredictably and frequently. Users don’t need to know the
details of the network, even the resources are constantly changed.
•
Performance and Scalability: the performance should stay stable when the number
of nodes are increased dramatically, such as constant response time, linear growth
of aggregate storage space, and constantly increasing search.
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•
Reliability: it should have the ability to defense external attacks without
significant data or performance loss.
•
5.1.2
Anonymity: it keeps every peer’s private information secured.
P2P Networks
This section reviews the three most popular p2p networks: eDonkey2000, Gnutella, and
BitTorrent.
1. eDonkey2000
The eDonkey2000 (nicknamed “ed2k”) file sharing protocol is implemented by the
original windows based eDonkey2000 client [143] developed by MetaMachine, using the
Multisource File Transfer Protocol, and additionally by some open-source clients like
MLdonkey [144] and eMule [57].
The ed2k network is a de-central hybrid peer-to-peer file sharing network with client
applications running on the end-system that are connected to a distributed network of
dedicated servers. The ed2k protocol uses two TCP ports (4661, 4662) and one UDP port
(4665) default. Data are transferred via TCP, and control packets (such as search related
packets) are transferred by either TCP or UDP.
Contrary to the original Gnutella protocol it is not completely de-central as it uses servers;
contrary to the original Napster protocol it does not use a single server (farm) which is a
single point of failure, instead it uses servers that are run by power users and offers
mechanisms for inter-server communication. Unlike super-peer protocols like KaZaa, or
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the modern Gnutella protocol, the ed2k network has a dedicated client/server based
structure. The servers are slightly similar to the KaZaa super-nodes, but they are a
separate application and do not share any files, only manage the information distribution
and work as several central dictionaries which hold the information about the shared files
and their respective client locations.
In the ed2k network only clients share data, as severs index their shared files when
servers are connected. When a client starts to download a file or a part of a file, firstly it
connects to a server via TCP or sends a short search request via UDP to one or more
servers to get the necessary information about other clients offering that file.
The ed2k network is using 16 byte MD4 hashes to (with very high probability) uniquely
identify a file independent of its filename. The implication for searching is that two steps
are necessary before a file can be downloaded in the ed2k network. First, a full text
search is made at a server for the filename, it is answered with those file hashes that have
a filename associated which matches the full text search. In a second step, the client
requests the sources from the server for a certain file-hash. Finally, the client connects to
some of these sources to download the file.
A typical ed2k link includes the filename and the filesize besides hash codes. An example
(a link to the 645.1Mb TV show “The pacific” ep1) is provided below:
ed2k://|file|The.Pacific.Part.1.Chi_Eng.HRHDTV.AC3.1024X576.x264.mkv|676471938|9a617a5c62a9e2294f0be9b10704c895|h=zunwifgbixqr5kgrtj
xkoctaf2wxegub|/
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The "file" part indicates that this is a file link, as opposed to a server link. An optional
AICH top hash |h=H52BRVWPBBTAED5NXQDH2RJDDAKRUWST| is also included
to help recover the file in case of corruption during file transfer.
In the eMule client, ed2k data packets are transferred as blocks of 10,240 bytes in 8
successive packets. All ed2k control packets are wrapped in a special header that starts
with the eight-bit character 0xe3, followed by an unsigned long that specifies packet
length. On the other hand, ed2k control packets start with a different eight-bit character,
0xc5.
2. Gnutella
The Gnutella protocol [56] is an open, decentralized search protocol for file sharing.
Specifically, it uses HTTP packets for file transfers. Gnutella uses port 6346 and 6347 for
TCP and UDP traffic as default reports, and users can change the ports in Gnutella clients
easily. Known Gnutella clients include Limewire, phex, BearShare, and Morpheus. The
block sizes depend on each Gnutella client, e.g., Morpheus uses 32,768 bytes, and
Limewire uses 100,010 bytes.
Gnutella nodes work as SERVers and cliENTS at the same time, thus are called servents.
Via client software, users can send out keyword queries and retrieve search results, and in
the other way, accept search requests from other servents, check the search keywords
against their local files, and respond with appropriate and related results.
Gnutella uses bootstrap way to connect a node to the network. A new servent initially
connects to several known hosts that are always connectable to join the system. Once this
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new servent has one or more open connections with servent already in the network, the
servent sends messages to interact with each other to connect more servents in the
network. Messages are broadcasted (sent to all servents) or simply back-propagated (sent
on the reverse of the path taken by an initial message). To send broadcasted/backpropagated messages, each message has a randomly generated and unique identifier, TTL
(time-to-live) field, and “hops passed’ field, and in the mean time, each servent keeps a
short memory of the recently received messages, used to prevent re-broadcasting and
implement back-propagation.
There are mainly three types of messages in the network. Group membership (PING and
PONG) messages are broadcasted to announce a new node joining the network, and also
share IP addresses and the number and size of shared files. Search (QUERY and QUERY
RESPONSE) messages are broadcasted to the network including user specified search
keywords. Each receiving servents matches the keywords against to the local shared file
list. When matches are found, QUERY RESPONSES are back-propagated to the
requestor including necessary information to download a file. File Transfer (GET and
PUSH) messages are encapsulated as HTTP packets, which are used to file downloading.
As we mentioned above, Gnutella network is dynamically changed, where nodes join and
leave the network very frequently. To understand the current Gnutella network, a node
periodically PINGs its neighbors to discover other active nodes. By this mechanism, a
node which drops connections because of unstable network can always reconnect back to
the Gnutella network.
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3. BitTorrent
BitTorrent [53] is a P2P network targeting to leverage the upload bandwidth of the
download peers and achieve fast and efficient large files sharing. During a file transfer in
a BitTorrent network, the file is divided into equal-sized blocks (typically 32-256 KB in
size), and nodes can download different blocks from multiple peers simultaneously.
BitTorrent uses 6881 as the default port, but most BitTorrent clients support transfer files
on arbitrary ports, such as µTorrent, rtorent, Azureus, and Transmission etc.
Unlike Gnutella networks, BitTorrent networks have some special set of nodes, called
Trackers, which keep track of the nodes currently in the system. The tracker receives
updates from nodes when nodes join or leave the torrent or periodically (i.e., every 30
minutes), and also sends nodes which share the same file to the node requests the chunks
of the file. The tracker information is included in a “.torrent” file, which is used to
identify a file to be shared on BitTorrent networks.
Nodes in the BitTorrent network can be divided into two groups based on the percentages
of the file the node holds. If nodes have a complete copy of the file and share the file to
others, these nodes are called seeds. Otherwise, nodes that only hold part of the file and
are still downloading the file are called leechers.
The basic process for a new node to download a file is as the following: a) when a new
node loads the torrent file, it tries to contact the tracker to retrieve a list containing a
random subset of the nodes that share the file completely or partially; b) the new node
then attempts to establish connections to about the nodes in the list got from the tracker;
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the node can contact the tracker periodically to obtain additional peers; c) the new node
now tries to download blocks from and upload blocks to the peers it connects to; the
blocks which are least replicated among its neighbors will be downloaded first.
5.1.3
Related P2P Research
To fight digital piracy, a lot of related P2P research studies have been conducted. They
are focused on the following three main categories: a) Impacts on industries and
strategies, b) characterization of P2P networks (topology, traffic etc.), c) anonymity and
privacy. Most studies have been concentrated to only a limited number of P2P protocols,
which generally include eDonkey2000, BitTorrent, and Gnutella because they are easy to
access and popular.
The first category consists of a bunch of studies about how P2P technology impact on
music, movie, software, publication, and computer games industries, and some other
general economic, social, and ethical implications of this technology. Lu [145] discussed
several critical issues caused by the current illegal use of P2P technology for sharing
copyrighted music: e.g., the serious damage to music production and the infringement on
copyright holders’ interests, but in the other hand, he agreed with the significance of P2P
as an advanced technology for popularizing music and sharing human and spiritual values
with more people. Walls [146] examined the rate of motion-picture piracy across a
sample of 26 diverse countries and conducted a cross-country regression analysis which
indicated that piracy is increasing in the level of social coordination and the cost of
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enforcing property rights, unrelated to income and decreasing in internet usage. Andersen
and Frenz [147] used representative survey data from the Canadian population collected
by Decima Research to quantify how the downloading of music files through P2P
networks influences music purchasing in Canada.
In the second category, researchers mainly measure and examine topological properties
of P2P networks [58] by monitoring P2P control packets, as they are small sized and with
rich information about connections inside the networks. In the study of [58], the topology
of the Napster and Gnutella networks were explored. The detailed measurements of P2P
networks are performed with crawler and probers. Sen et al. [59] analyzed flow-level data
from a large ISP to unleash the dynamic of P2P networks. Bhagwan et al. [150] recently
have studied the availability of Overnet by probing the crawled hosts and proved that the
IP based measurement in previous studies had dramatically under estimated the number
of peers. In [151], Qiao et al. compared Gnutella and Overnet, and figured out they are on
a similar dynamic level. Unfortunately, all of these researches only focus on a subset of
peers in networks.
Privacy and anonymity have become significant issues in P2P networks as more and
more efforts are applied to P2P networks by copyright holders, such as RIAA and MPAA.
People try to hide file transfers and their identities by various means (e.g., use of proxy
servers) in P2P networks. These efforts actually cannot ensure complete anonymity.
Although the pioneering Freenet network [148] and Mnet [149] offer true anonymous
P2P networks, they are not efficient or popular in file sharing.
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Currently there is very few research study focused on developing a systematic approach
to collect, track people’s activities on P2P networks, e.g. how they use different P2P
applications or clients to download or upload copyrighted contents, and create a high
quality data collection to discover the download patterns. Additionally, the collection can
easily help copyright owners to identify the details of infringements and send out DMCA
notices.
5.2 Implementation of Building ASKE Data Collection
There are three key components in the ASKE framework to build high quality data
collections: Resource Identifier, Spider Agents, and Content Filter. Unlike the web
documents, P2P data cannot be collected with the web crawlers or spiders. In this section,
we describe the details of implementation about these three components for
eDonkey2000, Gnutella, and BitTorrent.
5.2.1
Resource Identifier
Given a copyrighted material, the resource identifier is responsible for looking for the
resources to download the given material, which usually include title, time, artist, and
copyright owner information. There are mainly two ways in P2P networks to identify
resources. The first way is that no matter what copyright contents are tracked, we collect
any resources in P2P networks; the other way is based on the given title, time, artist, and
copyright owner information, to use the search functions provided in P2P networks to
find the related resources. To get a complete cover range, we implement both
methodologies in eDonkey2000, Gnutella, and BitTorrent.
1. eDonkey2000
There are a lot of ed2k websites to publish ed2k links for movies, tv shows, music etc. on
the Internet, such as VeryCD (http://www.verycd.com) and ShareReactor
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(http://sharereactor.com). Also there are some other web sites providing the search
functions for ed2k links, e.g. FileDonkey (http://filedonkey.com/) and Figator
(http://figator.com/). The systematic process to locate and archive ed2k links from web
sites are described as follows:
•
•
•
•
•
Compile a list of titles that are popular or concerned by copyright owners
Identify websites that post and update ed2k links frequently: compile a list of high
quality web sites with domain experts’ help; use popular pirated titles to search on
general search engines to locate this kind of websites.
Crawl ed2k links: build an automatic web crawler to grab the ed2k links from the
web sites identified in last step periodically.
Search on ed2k link search engines: with the list of titles, we can search on ed2k
link search engines to identify the related ed2k links periodically.
Archive ed2k links: since all ed2k links are in similar formats, it’s easy for us to
store them in a database with file name, file size, and hash codes; here we use
hash codes as distinct index to identify ed2k links uniquely, as some ed2k links
may use different file names with same hash codes.
The process to collect ed2k links in ed2k networks is a little bit different.
•
•
•
•
•
Build fully compatible ed2k resource agents: since we need to search ed2k
networks directly, an agent that can connect and search to ed2k networks has been
built.
Maintain a list of ed2k servers: ed2k servers manage the information distribution
and work as several central dictionaries which hold the information about the
shared files and their respective client locations; so a list of high quality and
popular ed2k servers has to be built and maintained, given ed2k servers are got
shutdown very frequently.
Connect to ed2k servers: to be more efficient, we start tens of ed2k resource
agents to connect multiple ed2k servers at the same time.
Search on ed2k networks: each ed2k server will return ed2k links matched to
titles and corresponding content types; the main issue here is that most ed2k
servers have self protection mechanism, meaning when requests from same IP
address hit the server too often, it will automatically reject the connection from
that IP address, in this case, we have to control the ed2k resource agents to search
ed2k servers with some random delays.
Archive ed2k links: this step is same as above.
2. Gnutella
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The resource identifier for Gnutella is kind of simple, since in Gnutella Networks, there
is no central server. The Gnutella resource agents we built connect to as many super
nodes as they can and send out search requests containing title keywords and content
types.
3. BitTorrent
In BitTorrent networks, contents are distributed via torrents, which are small files around
few kilobytes and contain file names, their sizes, trackers, and checksum info etc. The
BitTorrent resource identifier focuses on how to collect torrents on the Internet, parse
them, and store them into databases.
The process to locate and archive torrent files is similar to ed2k, as there are huge amount
of web sites to publish torrents in different categories, such as ThePirateBay
(http://thepiratebay.org) and Monova (http://www.monova.org). More torrent search
engines are available also, including isoHunt (http://isohunt.com) and Torrentz
(http://www.torrentz.com). The behavior of the BitTorrent resource identifier is similar to
the one for ed2k:
•
•
•
•
•
•
5.2.2
Use title keywords to search general search engines to identify torrent URLs
Maintain a list of torrent web sites and torrent search engines
Grab torrent URLs from torrent web sites
Search title keywords in torrent search engines and get torrent URLs
Download torrents via the torrent URLs identified from the steps above
Parse torrents to get file names, file sizes, trackers, and hash codes which are the
unique identifier for torrents
Spider agents
The perfect spider agents would be complete and instantaneous for any given resources
(ed2k links or torrents). However, in practice such perfect spider agents don’t exist as the
following conditions cannot be met:
•
Rapidly Changing Topology: spider agents target to create a perfect and complete
snapshot of the P2P network topology in a very short time delay. However,
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spiders actually contact peers progressively. Therefore, the connection speed of
the spider and its limited hardware resources would make capturing a complete
snapshot take a lot of time. The more time the spider contact peers, the more
distorted the captured snapshot is compared to the instantaneous ideal as more
peers join or leave during the spidering process.
•
Unreachable Peers: the snapshots collected by spiders are often incomplete
because there are always unreachable peers, which are either located behind a
firewall or overloaded. We can make the spiders more persistent with longer
timeouts when connecting peers, but in the other hand, it will increase the spider
duration and thus generate distortion.
There is no perfect solution to solve these two challenges. The way to mitigate the
problems is to develop fast spider agents and run as many spider agents as we can
concurrently. In this section, we discuss how to develop fast and efficient spider agents
that can capture snapshots for P2P networks. Figure 5.1 presents the high level
architecture of P2P spider agents.
Our P2P spider agents mainly deploy the following features to collect peers’ activities, as
described below.
1. Distributed Architecture
In order to archive a high degree of concurrency and to effectively utilize available
resources on multiple servers, the spider agents employ a three-level master-slave
architecture, described as in Figure 5.1.
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The first level is Top Master Process (TMP), which is responsible to extract resources
(ed2k links, torrents) from the resources database, and dispatch the resources to the
second level Sub Master Process (SMP). The SMP on the second level connect to P2P
networks, and collect peers that associate to the resource which is uniquely identified by
a hash code. The Sub Master Process schedules multiple slave processes on the third
level that act as virtually independent spiders and communicate with the peers in parallel.
Each slave has an independent queue of finite number elements to contact, which are
filled by SMP. An element in the queue includes the peer information and the resource
identifier (usually are hash codes). The whole procedure would never stop, since TMP
will keep retrieving from the resources database recursively.
Figure 5.1 The Architecture of P2P Spider Agents
148
2. Asynchronous Communications
Each slave process reaches hundreds of peers in parallel to get peer information
asynchronously. To make sure all salve processes are busy but not overloaded, we
implement an adaptive schedule algorithm based on each slave node’s resources. Based
on the reported resource usage for each slave, the number of peers that are assigned to
each slave is adjusted accordingly.
Each slave maintains a threshold, call max_slaves, which makes sure that there would be
no new connections when the maximum number of open connections is achieved.
3. Appropriate Timeouts
Many peers are unreachable because of firewalls or overloaded. As we described before,
we need to find appropriate timeouts because of the tradeoff when increase and decrease
timeout values for spidering. If we set low timeouts, e.g., less than 10 seconds, we will
see a lot of unreachable peers; in the other hand, if we set high timeouts, e.g., 5 minutes,
the spider agents run with very low efficiency. In our system, we set the timeout to 30
seconds, which is appropriate for our P2P applications. For the peers that are overloaded,
we can always try to connect in next round.
4. Utilization of P2P client features
Some protocol features would help a lot to promote spider agents’ efficiency. For
example, Gnutella clients implement a two-tiered network structure with ultrapeers and
leaf peers. Most of the peers are leaves that are connected to several ultrapeers. So that
149
when connecting to an ultrapeer, spider agents explore all the leaf peers that are
connected to the ultrapeer. By this approach, we are able to capture all the nodes and
links by connecting a relatively small number of peers. In addition, in most popular
Gnutella clients, such as LimeWire, they allow a quick query to a peer to receive details
of peer information, thus spider agents can learn the addresses of the peer’s neighbors,
and for an ultrapeer, the addresses of its leaves.
5.2.3
Content Filter
The noise or garbage data exists in the whole process of building data collection. When
we collect resources for a movie title “Star Trek”, the resource identifier may mistakenly
collect torrents for “Star Trek” TV shows. Since P2P networks are unmanaged, anyone
could create fake ed2k links or torrents that actually contain garbage data, for example a
file with all 0 filled, and named as “avatar.3d.2009.blu-ray.mkv”, or copyright owners
may poison P2P networks for their copyright contents. Also some bad guys would spoof
IP addresses with bogus IPs that actually don’t exist. Content Filter is responsible to rate
resources to correct titles, clean up fake resources, and identify bogus IPs.
1. Rate names
To rate a filename or a folder name to its corresponding title correctly, we have to
understand the naming rules in P2P networks. Here we use movie filenames and folder
names as the example. The following is a typical filename of a movie release:
Title.of.the.Movie.YEAR.Source.[Language].Codec-GROUP[.Filetype]
150
•
YEAR: the release year of the movie
•
Source: it’s pirated movie release type with respective sources, ranging from the
lowest quality to the highest, including Cam (CAM), Telesync (TS, TELESYNC,
PDVD), Workprint (WP), Screener (SCREENER, DVDSCR, DVDSCREENER,
BDSCR), R5 (R5), Telecine (TELECINE, TC) , DVD-RIP (DVDRip) , DVDR
(DVDR), HDTV (TVRip, DSR, STV, PDTV, HDTV, DVBRip), BD/BR Rip
(BDRip, 1080p.Blu-Ray, 720p.Blu-Ray, Blu-Ray)
•
Language: this field is optional, it specifies the language in the movie, e.g.
English, Spanish, Italian, etc.
•
Codec: it describes what video standard this release follows, such as VCD, SVCD,
DivX, Xvid, x264, h264 etc.
•
Group: it identifies the group name who compresses this movie and releases it on
P2P networks.
•
Filetype: the file type of the file, such as avi, mkv, mp4, mpg, and rmvb etc.
A typical movie release will look like:
Garfield.A.Tail.Of.Two.Kitties.2006.720p.BluRay.x264-AVS720
Here “Garfield A Tail Of Two Kitties” is the movie title; the theatre date of the movie is
2006, the source of this release is Bluray; the resolution of the release is 720p, which
means 1280x720; the codec is x264; the release group is called AVS720.
With the understanding of naming rules, we can build a rating engine to map resources to
titles:
151
•
Compile a complete title list of a given copyrighted material: for example, the
movie “Garfield A Tail Of Two Kitties” has another name “Garfield II” or
“Garfield 2”.
•
Extract title and year info from filenames or foler names: remove other
information about the file or folder
•
Process special characters: like “.”, “-”
•
Calculate the similarity between the processed file name or folder name with the
given title: if the similarity is higher than 90%, then we believe this resource is for
this title
2. Remove fake resources
We apply two methods here to remove fake resources.
•
0day check: it is based on an assumption, “0day releases are always the first
releases in P2P networks”. 0day refers to any copyrighted work that has been
released the same day as the original product, or sometimes even before. It was
considered a mark of skill among warez distro groups to crack and distribute a
program on the same day of its commercial release. We collect all 0day releases
details (names, groups, dates) from VCD Quality (http://www.vcdq.com) and
NfoDB (http://nfodb.com). Any resources that we find in P2P networks and are
earlier than the first 0day release are rated as “FAKE”
•
Chuck check: this methodology is based on the empirical research. A lot of fake
files on P2P networks are actually filled with all zeros. We use spider agents to
152
download chunks of the file and check if the chucks are all zeros. Though this
method is a little bit slow, it’s useful when the first method is not applicable, e.g.
0day release has shown up.
3. Identify bogus IPs
When spider agents retrieve IP address list from ed2k servers, BT trackers, or Gnutella
ultrapeers, the list may have been contaminated by bogus/spoofed IPs. Instead of handing
the IP list directly to slave processes to check, we need to filter out these bogus IPs first
for efficiency consideration. A lot of the bogus IPs are actually unallocated. It’s easy for
us to filter them out by comparing them against the bogon list from bluetack
(http://bluetack.co.uk/).
5.3 Services and Case Study
With the supports from efficient spider agents, we can provide copyright owners
multiples services that help them understand how, when, and where their materials is
being pirated online as a means of protection, enforcement, and comparative analysis to
more effectively and efficiently plan for the present and the future. In this section, we
present how users can use KFC to define their profiles and reports. In addition, a case
study on the movie Watchmen is reported.
5.3.1
Services for Copyright Owners
153
To help copyright owners understand the whole picture of their content piracy in P2P networks,
we create an Anti-Piracy system. Users can login the site and customize their profiles. KFC in the
ASKE framework is actually the back end to the customized services. As the data we collected
are stored in structured databases, the search function is kind of common to just query the
databases.
1. Customized Profile
Figure 5.2 presents the interface for users to customize their profiles. Below are the settings users
can adjust for a better user experience and for displaying only the information you would like to
see when navigating through the system.
•
Location: This setting allows users to choose the country or region in which users are
currently located.
•
Language: This setting will customize the language displayed in the interface based on
the language chosen. The interface supports all languages but is dependent on the
language packs installed in users’ browsers.
•
Date/Time Format: This lets users chose US, European or internet ISO date
representation format.
•
GMT setting: The GMT setting lets users chose your local time zone so that when time
related information is displayed the interface or reports will calculate users’ time zone.
•
SMTP server: This setting controls from where the system sends the email messages and
reports.
•
From Field: This field represents the email address that will be displayed to the recipient
of a report that has been sent out. If someone selects to use the reply function in their
email client this is where the reply would be routed to.
154
•
Reply to Field: This works the same as the last section does. However this is also used
by the different email application for rules and filtering.
•
My overview settings: These settings let users choose how to customize the overview
page in the system and in reports. It allows users to choose the columns of the 3 sections
on the page and select or deselect columns to display.
•
Interested titles: This function provides an entry for users to upload the details of titles
they are interested in to follow up in P2P networks. The input file can be an excel file or
an csv file in pre-defined format.
Figure 5.2 Customize user profile
2. Overview Page
155
The overview page provides users the quick snapshot of targets and pirates as Figure 5.3,
such as Today’s Top Pirates, Monitoring, and Fresh Title Releases. Each entry has a
magnifying glass for users to check the detail record of the target, such as time, IP
address, port, infringed title, infringed file name, hash codes etc.
Figure 5.3 Infringement overview page
3. Search Interface
Users can search the details of piracy via IP, Zipcode, title etc. Search functions here are
used to reveal relations and associations between targets on different protocols.
Figure 5.4 Infringement search interface
4. Analysis Report
156
With solid piracy data stored in the database, the Anti-Piracy system can generate reports
with business intelligence easily. For example, we can figure out the top 5 infringing
countries based on trackers count and peers count during a given period for a movie title,
see Figure 5.5.
Figure 5.5 Top 5 countries of peers and trackers for “Quantum of Solace” between 11/03/2008 to
11/21/2008
157
5.3.2
Case Study – Watchmen
To illustrate the usage of the ASKE framework applied in P2P world, we conducted a
case study on a movie Watchmen for a month from 3/6/2009 to 4/5/2009 to generate a
high-level overview of global P2P piracy activity. The main P2P protocols we monitored
here are eDonkey and BitTorrent.
Watchmen premiered on March 4, 2009, to a worldwide audience filled with tremendous
hype and anticipation. In the United States, it was the all-time widest-release of an R
rated film. As of April 13, 2009, it is the #1 highest-grossing R rated film of 2009. It is
also the #1 highest-grossing Alan Moore adaptation. Such a ground-breaking film is
bound to be a catalyst for significant online piracy.
Watchmen was first made available online on Friday, March 6, 2009. Since then, the film
has been downloaded over 730,000 times in 213 countries by approximately 627,000
distinct individuals. It has been uploaded over 795,000 times from 16 countries by
approximately 92 servers.
The most popular versions of the film released across all protocols were in English and
were telesync versions of the film. The quality is quite inferior relative to a DVD. Piracy
surrounding the film is expected to surge to much higher levels as soon as better quality
versions become available. Appendix F list the resources (ED2K links and BitTorrent
files) we located for Watchmen.
158
Figure 5.6 presents the distribution of piracy among different protocols. BitTorrent is by
and large the most popular protocol available for sharing content online. 91% of total
infringements for Watchmen occurred on the BitTorrent protocol, with 9% occurring on
eDonkey. This is common for film titles because of the size of files involved. BitTorrent
is the ideal protocol for sharing large files because it does not cause network strain, nor
does it require exhaustive amounts of bandwidth on one particular server or network. By
spreading the network load, facilitators (seeders), peers and trackers can easily
communicate and share content with minimal resources.
Figure 5.6 Protocols breakdown
In Figure 5.7, we present the daily trend of infringements. The overall trend for total
infringements of Watchmen is clearly downwards. This is common for a film which does
not have high-quality pirated versions available. The most piracy occurs within one week
159
of general release; especially when there is not a tiered release schedule beyond a few
days.
In this case study, we also identified the top 10 ISPs with most peers which were pirated
the movie. Telecom Italia is the leading provider of broadband service in Italy, and
singlehandedly represents the majority of peer infringements worldwide originating from
one ISP. Telefonia de Espana had slightly less infringements. Open Hosting Limited
was split approximately 60% to 40% in Germany and the United Kingdom. Deutche
Telekom AG is based in Germany. SBC Internet Services and Comcast Cable, and Road
Runner are the leading ISPs in the United States. France Telecom had the sixth-most and
represents peer infringements originating from France, Algeria, French Guiana,
Guadeloupe, Madagascar, Martinique, Niger, and Reunion. British Telecommunications
is based in the United Kingdom, and Polish Telecom is based in Poland.
The top five releases were in English, followed by an Italian version, and two German
versions. These releases were primarily telesync versions of the film, with cam versions
available as well. During the testing period, there were no high-quality pirated releases
of the film. In total, there were over 150 pirated versions of film available worldwide.
160
Figure 5.7 Daily infringements trend – all infringements
Figure 5.8 Top 10 ISPs
161
Figure 5.9 Top 10 filenames
5.4 Conclusions
In this chapter, we mainly discussed how to monitor P2P networks using ASKE
framework. We reviewed different P2P protocols and current research topics in P2P
networks. The detail of implementation of building data collection is presented. Also we
reported the Anti-Piracy system which uses the P2P data collection to serve users with
customized profiles. At last, we presented our case study results for monitoring the movie
Watchmen piracy on eDonkey network and BitTorrent Network.
162
CHAPTER 6
CONCLUSIONS AND FUTURE DIRECTIONS
The Internet provides the largest knowledge repository across domains, applications, and
countries. It is desirable for researchers, managers, and government agencies to access,
analyze and share such huge information. This dissertation proposes an application
specific framework to support Internet searching, browsing, and analysis with effective
and efficient approaches. This chapter summarizes the main conclusions and
contributions of this dissertation, and suggests future directions.
6.1
Conclusions
In this dissertation, we explore an effective ASKE framework to build structured and
semantic data repositories, and support keyword search and semantic search. The
framework is consistent with the architecture of most search engines. It enhances three
extensions to this basic structure: various data retrieval ability; semantic data support; and
post-retrieval analysis. Various techniques and algorithms that could facilitate knowledge
discovery are applied in the framework.
By reviewing the characteristics of different data on the Web, we recognize that we have
to figure non-traditional ways to approach the problems that are resulted by these features.
In Chapter 2, we explore unstructured and structure data, data on online forum and social
networking sites, and P2P data.
163
To see how the framework can be applied into specific domains, we describe how to
develop an experimental Web-based counterterrorism knowledge portal, called the Dark
Web Portal, to support the discovery and analysis of Dark Web information and provide
an intelligent, reliable, interactive, and convenient interface with for the counterterrorism
experts. Systematic approaches to identify resources, collect different types of Dark Web
data, and build semantic data collection are presented in details.
Monitoring P2P data on the Internet is another study with ASKE framework. We describe
how to build an anti-piracy system, which generates insights of P2P download activities
and provides an effective anti-piracy strategy to combat piracy in P2P networks. We
propose a feasible approach to implement the components in ASKE for different P2P
protocols, and report the challenges and problems during the process we implement them.
The anti-piracy system provides customized service to users via KFC. A case study
focused on the movie “Watchmen” is discussed with details based on the data collection
we built via the ASKE framework.
6.2
Future Directions
I plan to expand my research in several directions. First, I plan to continue development
of effective and efficient techniques and algorithms that support building semantic
repositories and semantic search, in particular, the implementation of Metadata Extractor.
Secondly, I plan to conduct large scale interactive user evaluations of such application
specific Web portals. This is an area has not been investigated. I am also interested in
164
continuous investigation and measurement of P2P networks, as the growth and changes
keeps happening in P2P world. In addition, I will experiment the framework in more
application domains, such as some scientific research domain, e.g. intelligent
transportation systems.
165
APPENDIX A
US DOMESTIC EXTREMIST GROUPS AND URLS
"1" represents that the group name is listed at the specific resource. For "White Supremacy", only SPLC listed different branches of
this group, while other resources use KKK as a general group name. For consistency, we add frequency count to all the branches listed
accordingly, assuming all branches listed by SPLC are considered part of the bigger "KKK" group by other resources too.
Acronyms:
ADL - Anti-Defamation League
FBI - Federal Bureau of Investigation
SPLC - (Southern Poverty Law Center)
Black Separatist
ADL
FBI
SPLC
United Nuwaubian Nation of Moors
1
New Black Panther Party
1
Nation of Islam
Christian Identity
America's Promise Ministries
Artisan Publishers
ADL
FBI
1
SPLC
1
1
Militia
Watchdog
Web
Directory
Hate
Directory
1
Militia
Watchdog
1
Web
Directory
Total
URL
1
3
Total
http://www.geocities.com/Area51/
Corridor/4978/unnm.html
http://hamp.hampshire.edu/~cmnF
93/panthers.html
http://noi.org/
URL
1
1
http://amprom.org
http://www.artisanpublishers.com
2
1
Hate
Directory
166
Aryan Covenant Church/ACC Services
By YahwehÕs Design
Church of Christ in Israel
Fellowship of GodÕs Covenant People
Gospel Broadcasting Association
Upper Room Identity Fellowship
Virginia Christian Israelites
Dogma of Christian Identity
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Rick Ross
Cry Aloud Cybermagazine
Harp Of David Ministries
1
1
1
1
1
1
First Century Christian Ministries
1
1
Christian Research
Christian Separatist Church Society
Church of the Sons of Yhvh
1
1
1
1
1
1
2
2
2
Church of True Israel
1
1
2
Covenant Church of Yahweh
Crusade for Christ
Gospel Ministries
House of Yahweh
Kinsman Redeemer Ministries
Lord's Work
1
1
1
1
1
1
1
1
2
2
2
2
2
2
Mission to Israel
New Covenant Bible Church
1
1
1
2
2
Virginia Publishing Company
Weisman Publications
1
1
1
1
2
2
1
1
1
1
1
http://www.geocities.com/onemans
mind/
http://www.rickross.com/
http://www.cryaloud.com/
http://harpofdavid.homestead.com/
CELTIC3.html
http://www.secondexodus.com/
html/russford/firstcenturychristian
ministries.htm
http://www.equip.org/
http://www.christianseparatist.org/
http://www.aryannationsknights.co
m/socy2.htm
http://www.churchofthesonsofyhvh
.org/
http://www.churchoftrueisrael.com
/
http://www.nccg.org
http://www.ccci.org/
http://www.gospelfortoday.org/
http://www.yahweh.com/
http://www.kinsmanredeemer.com/
http://www.freespeech.org/thelords
work/
http://www.missiontoisrael.org/
http://www.covenantbiblecollege.a
b.ca/
http://www.wordnews.com/
http://www.seek-info.com/
167
Westboro Baptist Church
Christian Legal Reformation Club
1
1
1
1
2
2
Northwest Kinsmen
1
1
2
Christian Separatist Church Society
Mission to Israel
Church of Jesus Christ
Scriptures for America Ministries
1
1
1
1
1
2
2
3
3
1
Hate
Directory
4
Total
Kingdom Identity Ministries
Militia
42nd/58th/59th Brigades Missouri
Militia
The Ohio Unorganized Militia
Assistance And Advisory Committee
The Viper Reserves
1
1
ADL
FBI
1
SPLC
1
1
Militia
Watchdog
1
1
1
1
Web
Directory
1
1
1
1
1
Minnesota Minutemen Militia
1
1
Republic of Texas Defense Force
1
1
Central Ohio Unorganized Militia
California Militia
1
1
1
1
North Carolina Citizen Militia
1
1
Michigan Militia Home Page
Militia of Montana
1
1
1
1
The Constitution Society
Louisiana Unorganized Militia
1
1
1
1
http://www.godhatesfags.com/
http://www.clrc.net/
http://www.clrc.net/clrc.html
http://www.cris.com/~nwk/nwk.ht
m
http://www.christianseparatist.org/
http://www.missiontoisrael.org/
http://thechurchofjesuschrist.com/
http://www.scripturesforamerica.or
g
http://www.christianidentity.org/ho
me.htm
http://kingidentity.com/
URL
http://www.geocities.com/CapitolH
ill/4274/
http://www.oumaac.com
http://www.geocities.com/CapitolH
ill/Lobby/8786/viper.htm
http://www.geocities.com/CapitolH
ill/Lobby/6745/default.html
http://web2.airmail.net/reptex1/def
ault.htm
http://www.infinet.com/~pandar/
http://pw1.netcom.com/~stevep/we
lcome.html
http://www.netpath.net/~jeffr/nccm
.htm
http://militia.gen.mi.us/
http://www.nidlink.com/~bobhard/
mom.html
http://www.constitution.org
http://www.orioncs.com/freedomforum/
168
Southeastern Ohio Defense Force
1
1
7th Missouri Militia
1
1
Southeastern States Alliance Militia
Headquarters
United States Theatre Command
The Real Deal
1
1
1
1
1
1
Pomona Valley Militia
1
1
The 2nd Cavalry - 14th Division
Kentucky State Militia page
The Palmetto State Guard
1
1
1
1
Militia Channel Homepage on Efnet
Ted Davis Memorial Brigade
1
1
1
1
Montana Unorganized Militias
1
1
Militia Report - Columbiana County Ohio
508th Airborne Regiment - Texas
National Defense
North Mississippi Militia
1
1
1
1
1
1
Arizona Unorganized Militia
1
1
The Guardians
Ramblings
1
1
1
1
South East Texas Defense Force
1
1
Texas Shock Front Elite
1
1
http://members.aol.com/RMORGA
N762/default.html
http://www.monet.com/~mlindste/7momilit.html
http://www.geocities.com/CapitolH
ill/9852/saa.htm
http://www.bignet.net/~eagleflt/
http://www.sfol.com/sfol/santafe/re
aldeal/default.html
http://home.earthlink.net/~wsranch
/webdocs/Pomona_Valley_Militia.
htm
http://www.geocities.com/CapitolH
ill/Lobby/9659/default.html
http://www.geocities.com/CapitolH
ill/Senate/4952/default.html
http://www.mdc.net/~militia/
http://www.ocnsignal.com/y2kmilitia1.htm
http://www.angelfire.com/mt/militi
awar/militia.default.html
http://hometown.aol.com/locowrpo
ny/
http://welcome.to/508thAirborne
http://members.xoom.com/nm_mili
tia/index2.htm
http://www.americanunlimited.com/arizonauno.htm
http://www.guardians.org/
http://www.coolmedia.net/cbg/ram
ble/ramblings.html
http://www.geocities.com/Pentago
n/8410/setdf.html
http://members.xoom.com/TexasS
FE/
169
New Mexico Citizens Regulated Militia
1
1
Wayne County Militia of Michigan
1
1
South Carolina Militia Corps
1
1
Thirteenth Texas Infantry Regiment
1
1
Iowa Unorganized Militia
Militia of Montana
1
1
1
1
Virginia Citizen’s Militia
Viper Militia
Washington County (Maine)
Constitutional Militia
Hudson Highlands Free Militia
1
1
1
1
1
1
1
1
California Militia Training Center
Danville Militia
1
1
1
1
Pennsylvania State Military Reserve
Pomona Valley Militia
1
1
1
1
South Dakota Unorganized Militia
1
1
Marietta Militia
1
1
Michigan Militia of Wayne County
New Jersey Militia
1
1
1
1
Militia of North Dakota
1
1
Minnesota Minuteman Militia - Fifth
Brigade
1
1
http://www.nmex.com/militia/milit
ia.htm
http://www.geocities.com/CapitolH
ill/1392/index2.htm
http://pw1.netcom.com/~dan3/scm
c.htm
http://freeweb.pdq.net/metalryder/1
3thTIR/
http://www.angelfire.com/ia/IUM/
http://www.montana.com/militiaof
montana/
http://vcm.freeservers.com/
http://www.vipermilitia.org/
http://www.angelfire.com/me2/Con
stitutionalMilita/default.html
http://www.angelfire.com/ny2/hhf
m/
http://home.earthlink.net/~jbk97/
http://members.theglobe.com/DEM
M/file_name.html
http://www.navpoint.com/~pasmr/
http://home.earthlink.net/~wsranch
/webdocs/Pomona_Valley_Militia.
htm
http://www.geocities.com/CapitolH
ill/Congress/7011/
http://mariettapa.com/marietta_mili
tia.html
http://www.michiganmilitia.org/
http://www.exit109.com/~njm/defa
ult.htm
http://community1.webtv.net/max722/MILITIAofN
ORTHDAKOTA/
http://members.xoom.com/mmmst
_cloud/
170
Missouri 51st Militia
Michigan Militia - Tenth Brigade
1
1
1
1
The Intelligence Report
California Militia
1
1
1
1
The Official Pack 44 Militia
1
1
The Patriot Underground
1
1
New Mexico’s Liberty Corps 3rd
Brigade
New Mexico Militia
Militia of Florida
Michigan Militia
The People’ss Militia
The Southern Indiana Regional Militia
1
1
1
1
1
1
1
1
1
1
1
1
The Citizens Militia of Maryland
1
1
So. Cal. High Desert Militia
1
1
New Mexico Militia
1
1
Militia of East Tennessee 3rd Brigade
1
1
Sons of Liberty Militia
1
1
United States Special Operations
Citizens Militia of Florida
Connecticut 51st Militia
Michigan Militia - 4th Division - 19th
Brigade - Roscommon County
Midsouth Liberty Alliance
1
1
1
1
1
1
1
1
http://www.mo51st.org
http://www.geocities.com/~cowbo
y140/
http://intelreport.freeservers.com/
http://www.geocities.com/CapitolH
ill/Congress/2608/welcome.html
http://marina.fortunecity.com/long
mark/311/
http://www.geocities.com/CapitolH
ill/Parliament/1691/
http://www.users.uswest.net/~toad
419/
http://www.zianet.com/toad419/
http://www.militia-of-florida.com/
http://www.michiganmilitia.com/
http://www.angelfire.com/il/fear/
http://www.fortunecity.com/victori
an/crayon/881/default.html/
http://www.expage.com/page/citize
nsmilitiaofmaryland
http://www.freeyellow.com/membe
rs8/highdesertmilitia/default.html
http://homes.acmecity.com/rosie/s
miley/145/default.html
http://www.geocities.com/met3rdbr
igade/
http://community1.webtv.net/We_The_People_/Son
sofLibertyMilitia/
http://hometown.aol.com/USSOC
M/page3/default.htm
http://expage.com/page/ctmilitia
http://www.geocities.com/Pentago
n/Bunker/6210/default.html
http://members.xoom.com/nm_mili
tia/
171
508th Regiment
Neo-Nazis
Aryan Nations COJCC
Aryan Nations-offshoot
Aryan Werwulfe Brotherhood
German American Nationalist PAC
Inter-National Socialist Party
Liberty Bell Publications
National Socialist German Workers
Party
SS Regalia
Nazi Low Riders
Women for Aryan Unity
The Library
Aryan Renaissance Society
National Socialist Vanguard
SS Enterprise
White Revolution
Knights of Freedom - Utah State
American Nazi Party
ADL
FBI
SPLC
1
Militia
Watchdog
Web
Directory
Hate
Directory
1
Total
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
2
2
3
1
1
1
1
1
1
1
http://randymiller.webjump.com/
URL
http://www.lbp2.com/
http://www.rac-usa.org/wau/
http://www.creator.org/womenforu
nity/
http://members.odinsrage.com/wau
/
http://www.wauhqs.cjb.net/
http://www.wcotc.com/womenforu
nity/
http://www.front14.org/wau
http://www.stormfront.org/crusader
/texts/wau/
http://www.crusader.net/texts/wau/
http://www.natall.com/aryanpage/wau/wau_index.html
http://ourhero.com/index2.html
http://members.odinsrage.com/ars/
http://www.alpha.org/nsv/
http://ssenterprise.tripod.com/book
shop.htm
http://www.whiterevolution.com/
http://www.io.com/~jack88/
http://www.americannaziparty.com
/
172
National Alliance
National Socialist Movement
New Order
Christian Defense League
Sons of Liberty
1
White Aryan Resistance
World Church of the Creator
1
1
1
1
1
1
1
1
1
1
1
Aryan Nations
1
1
1
Neo-Confederate
ADL
FBI
SPLC
The Confederate Society of America
South Carolina League of the South
The Southern Party
Council of Conservative Citizens
the League of the South
Southern Independence
League of the South
Racist Skinhead
ADL
FBI
1
SPLC
1
1
1
1
1
1
1
1
3
3
3
4
4
1
1
1
1
1
1
4
5
1
1
1
6
Web
Directory
Hate
Directory
Total
1
1
1
1
1
1
1
1
1
1
Militia
Watchdog
1
Web
Directory
1
1
1
2
2
2
Hate
Directory
3
Total
Confederate Hammerskins
Eastern Hammer Skins
Empire State Skinheads
National Racist Skinhead Front
Northern Hammerskins
Western Hammerskins
Celtic Knights
Keystone State Skinheads
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
Las Vegas Skinheads
1
1
2
Midland Hammerskins
1
1
2
http://www.natall.com/
http://www.nsm88.com
http://theneworder88.com/
http://www.cdlreport.com
http://www.geocities.com/sons_of_
liberty_mgs/index.html
http://www.resist.com/
http://www.anti-semitism.net
http://www.creator.org
http://twelvearyannations.com
http://www.aryan-nations.org/
URL
http://www.deovindice.org
http://www.palmetto.org/
http://www.southernparty.org/
http://www.cofcc.org/
http://www.dixienet.org/
http://members.aol.com/GrayFox6
5/south.html
http://www.dixienet.org
URL
http://front14.org/celticknights/
http://www.keystonestateskinheads
.com/
http://24.234.43.14
http://www.lasvegasskinheads.net
http://www.midlandhammerskins.c
om/
173
National Skinhead Front
Outlaw Hammerskins
1
1
Hammerskin Nation
American Front
1
White Supremacy
ADL
1
FBI
SPLC
Liberty or Death
Fourteen Words Press
American Coalition of Third Positionists
Heritage Lost Ministries
Voice of Citizens Together/American
Patrol
I Love White Folks
MOTM(Mothers of the Movement)
Templar Knights of the Ku Klux Klan
White Knights of the Sovereign State of
Missisippi
Crosstar - The Nationalist Movement
1
1
1
1
Web
Directory
1
1
1
1
2
2
1
1
2
2
Hate
Directory
Total
1
1
1
1
2
1
1
1
1
1
White Aryan Resistance
Confederate Knights of the Ku Klux
Klan
Divine Knights of the Ku Klux Klan
Militia
Watchdog
1
1
1
http://www.whitevictory.com/
http://www.outlawhammerskins.co
m/
http://home.att.net/~wpsh8814/
http://[email protected]
nfront.com/index2.htm
http://www.americanfront.com
http://web2.airmail.net/bootboy/af.
htm
http://www.geocities.com/CapitolH
ill/3343
URL
http://www.geocities.com/CapitolH
ill/Lobby/2945/
http://www.14words.com/
http://3rd.org/
http://heritagelost.org/frame/
http://www.americanpatrol.org/
1
1
0
2
2
3
3
http://www.ilovewhitefolks.com/
http://www.sigrdrifa.com/motm/
http://www.nationalist.org/default.
html
http://www.resist.com/
http://www.tommetzger.net/
http://www.tommetzger.org/
http://www.racehate.com/
http://www.free.cts.com/crash/m/m
etzger/
http://www.bayouknights.org/confe
derateknights.htm
1
1
1
3
1
1
1
3
1
1
1
1
4
1
1
1
1
4
174
Georgia White Knights of the KKK
Invisible Empire White Knights of the
KKK
1
1
1
1
1
1
1
1
4
4
Knight Riders of the Ku Klux Klan
1
1
1
1
4
Knights of Yahweh
1
1
1
1
0
4
Royal Confederate Knights of the Ku
Klux Klan
South Arkansas Knights
1
1
1
1
0
4
1
1
1
1
0
4
Southern Mississippi Knights of the Ku
Klux Klan
Alabama White Knights of the Ku Klux
Klan
American Knights of the Ku Klux Klan
America's Invisible Empire Knights of
the KKK
1
1
1
1
1
1
1
1
1
5
http://www.kukluxklan.net/
1
1
1
1
1
1
1
1
1
1
5
5
Bayou Knights of the Ku Klux Klan
1
1
1
1
1
5
Brotherhood of Klans
1
1
1
1
1
5
Confederate Crusaders
1
1
1
1
1
5
Free Knights of the Ku Klux Klan
1
1
1
1
1
5
Imperial Klans of America
International Keystone Knights of the
Ku Klux Klan
1
1
1
1
1
1
1
1
1
1
5
5
http://www.awkkkk.org
http://www.aiekkkk.hostmb.com
http://www.aiekkkk.org/
http://www.aie-usa.com/
http://home.hiwaay.net/~krotos/pic
turepage.html
http://www.bayouknights.org/
http://198.69.82.120/PirateWeb/Ba
youKnightsKKK/
http://www.bokkkkk.net/
http://www.bok.kukluxklan.cc
http://members.odinsrage.com/bok
kkkk
http://www.bayouknights.org/confe
derateknights.htm
http://www.geocities.com/freeknig
htsnc/
http://www.kkkk.net/
http://www.uniqe.com/kkk
http://iewk.kukluxklan.cc
http://konfederationklans.org/mem
bers/iewk/
http://home.beseen.com/politics/aki
a88/
http://members.tripod.com/knights
ofyahweh/
http://www.bayouknights.org/south
arkknights.htm
4
175
Invisible Empire Knights of the Ku Klux
Klan
Knights of the KKK
1
1
1
1
1
5
http://www.uniqe.com/kkk
1
1
1
1
1
5
Knights of the White Kamellia
1
1
1
1
1
5
Ku Klux Klan
1
1
1
1
1
5
Mississippi White Knights of the Ku
Klux Klan
Mystic Knights of the KKK
1
1
1
1
1
5
http://clubs.yahoo.com/clubs/knigh
tsofthekkk
http://www.kwknational.homestead
.com/index.html
http://www.angelfire.com/tx5/kwk
kkk/
http://members.theglobe.com/klan
man1/
http://www.acadian.net/~sandmanh
ttp://www.kamellia.com
http://shell.idt.net/~edoneil1/kkkho
me.html
http://www.mwkkkk.org/
1
1
1
1
1
5
National Knights of the KKK
1
1
1
1
1
5
North Georgia White Knights of the Ku
Klux Klan
1
1
1
1
1
5
Southern Cross Militant Knights of the
Ku Klux Klan
1
1
1
1
1
5
http://www.mysticknights.org/
http://mwkkkk.europeanwhiteknigh
ts.com/
http://mwk.kukluxklan.cc
http://akia.yoderanium.com/mwk
http://www.nkkkk.org/
http://www.cnkkk.org
http://nationalknights.org
http://nkkk.cjb.net/
http://www.geocities.com/national
_knights_kkk/
http://www.geocities.comnationalk
nightskkk/
http://www.neters.com/web/wwf.sh
tml
http://akia.cjb.net
http://members.surfsouth.com/~ng
wk/index.html
http://www.theklan.com/
http://militant2.homestead.com/
http://www.homestead.com/militan
176
Southern White Knights of the Ku Klux
Klan
1
1
1
1
1
5
SS Knights of the Ku Klux Klan
Texas Knights of the Ku Klux Klan
1
1
1
1
1
1
1
1
1
1
5
5
U S Klans
United White Klans
White Camelia Knights of the Ku Klux
Klan
Others
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
5
5
t/
http://groups.yahoo.com/group/swk
kkk
http://www.knightskkk.org
http://www.swkkkk.net
http://www.swkkkk.com
http://www.swkkkk.org
http://swk.kukluxklan.cc
http://louisiana.cjb.net/
http://southernwhiteknights.homest
ead.com/
http://www.sskkk.com
http://www.texasamericanknights.o
rg
http://klavalier.freeyellow.com
http://expage.com/unitedwhiteklans
http://www.wckkkk.com
ADL
FBI
SPLC
Web
Directory
Hate
Directory
Total
URL
Carnival Against Capitalism
Coalition to Save the Preserves
Creativity Movement
Earth Liberation Front
1
Elohim City
Greater Ministries International
Los Macheteros
New Black Panther Party for SelfDefense
Reclaim the Streets
Southeastern States Alliance
1
1
Sovereign Citizens
Workers' World Party
1
Militia
Watchdog
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
http://www.earthliberationfront.co
m/main.shtml
http://www.animalliberationfront.c
om/
http://www.macheteros.com/
http://www.reclaimthestreets.net/
http://www.geocities.com/CapitolH
ill/9852/saa.htm
http://www.workers.org/
177
United Fascist Union
1
1
American Falangist Party
National Patriotic Front
1
1
1
1
American Fascist Movement
1
1
Deseret National Socialist Syndicate
1
1
1
1
1
1
2
1
1
2
1
1
2
2
Folk And Faith
Animal Liberation Front
Armed Forces for Puerto Rican National
Liberation
Council of Conservative Citizens
Institute for Historical Review
1
1
http://www.geocities.com/Area51/
Chamber/7344/index.htm
http://www.falange.org/
http://www.geocities.com/Colosseu
m/Loge/8461/engl.html
http://www.americanfascistmovem
ent.com
http://libreopinion.com/members/d
nss/
http://www.folkandfaith.com/
http://www.animalliberationfront.c
om/
http://www.fas.org/irp/world/para/f
aln.htm
http://www.ihr.org
178
APPENDIX B
INTERNATIONAL TERRORIST GROUPS AND URLS FOR ARABIC
GROUPS
Group Name
Country
Region
Status
Source
Total
Rwanda
Africa
Active
USCFAFL
1
FLA
A
Niger
Africa
Central
Africa
Tentative
truce
USCFAFL
1
ORA
Niger
Africa
Central
Africa
Tentative
truce
USCFAFL
1
ARL
N
FPLS
Niger
Africa
1
Africa
USCFAFL
1
FARF
Chad
Africa
Tentative
truce
Tentative
truce
Active
USCFAFL
Niger
USCFAFL
1
FNT
Chad
Africa
Active
USCFAFL
1
Forces of Unity
MDD
Chad
Africa
USCFAFL
1
Legitimate
Command
National
Democratic
Alliance
Southern Sudan
Independence
Movement
Sudan Alliance
Forces
Sudan People's
Liberation Army
Umma Liberation
Army
People's
Democratic Army
Popular Front
CSNP
D
CNT
R
Chad
Africa
USCFAFL
1
Chad
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Janatha Vimukthi
Peramuna
Fatah wing
USCFAFL
1
CNR
Chad
Africa
Central
Africa
USCFAFL
1
FNT
R
PDF
Chad
Africa
Active
USCFAFL
1
Chad
Africa
Active
USCFAFL
1
UFD
Chad
Africa
Active
USCFAFL
1
FDD
Burundi
Africa
Active
USCFAFL
1
CND
D
FNL
Burundi
Africa
Active
USCFAFL
1
Burundi
Africa
Active
USCFAFL
1
Frolin
a
Burundi
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Central
Africa
Active
USCFAFL
1
PLPH
Burundi
Africa
Active
USCFAFL
1
Movimento de la
Izquierda
Revolucionaria
Corsican National
Liberation FrontTraditional Wing
Kanak Socialist
National Liberation
Front
Abkhazia rebels
Algetian Wolves
Tajik opposition
Pattani United
Liberation
Organization
Hizb el Nahda
Acro
nym
Detailed
Region
Central
Africa
Central
Africa
179
Active Al-Ittihad
al-Islami
Hizb-ul
Mujahideen
Red Brigades
Japanese Red Army
HezbollahGulf/Bahrain
Islamic Front for
the Liberation of
Bahrain
Movement for the
Liberation of
Bahrain
Shanti Bahini
ADF
Uganda
Africa
LRA
Uganda
Africa
UNR
F II
WNB
F
Uganda
Africa
Uganda
Africa
Somalia
Africa
Somalia
Africa
Somalia
SDA
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Active
2
Active
USCFAFL,
US
USCFAFL,
US
USCFAFL
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Africa
Eastern
Africa
Active
USCFAFL
1
Somalia
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
2
1
Fighting
Communist Cells
National Liberation
Army - Bolivia
Tupac Katari
Guerrilla Army
Forces for the
Defense of
Democracy
National Council
for the Defense of
Democracy
National Liberation
Forces
National Liberation
Front
Party for the
Liberation of the
Hutu People
Revolutionary
Subversive Faction
- Commando
Unabomber
Guatemalan
National
Revolutionary
Party
Chechen rebels
SNA
Somalia
Africa
SNF
Somalia
Africa
SNM
Somalia
Africa
Somalia
Africa
Somalia
Africa
Eastern
Africa
Active
USCFAFL
1
Somalia
Africa
Active
USCFAFL
1
USC
Somalia
Africa
Active
USCFAFL
1
USF
Somalia
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Active
USCFAFL
1
REN
AMO
Mozambi
que
Africa
Eastern
Africa
USCFAFL
1
MNR
Mozambi
que
Africa
Eastern
Africa
USCFAFL
1
Ethiopia
Africa
Active
USCFAFL
1
South Ossetian
rebels
Interahamwe
Militia
Movement of
Democratic Forces
of Casamance Northern Front
AIAI
Eritrea
Africa
Active
Eritrea
Africa
Active
USCFAFL,
US
USCFAFL
2
ELF
FRU
D
Djibouti
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Eastern
Africa
Active
USCFAFL
1
1
180
Movement of
Democratic Forces
of Casamance Southern Front
National Somali
Congress
Palestine Liberation
Front - Abu Abbas
faction
Mujahedin-e Khalq
Organization,
People's Mujahedin
Abu Nidal
Organization
People's Extra
Parliamentary
Opposition
Revolutionary
Communists' Union
of Turkey
Uganda National
Rescue Front II
West Nile Bank
Front
Continuity Army
Council
Party of
Democratic
Kampuchea
Front de Liberation
du Quebec
Armed Forces for a
Federal Republic
Chadian National
Front
Popular
Revolutionary
Forces Lorenzo
Zelaya
Al Faran
FRU
D
Djibouti
Africa
Eastern
Africa
Active
USCFAFL
1
Comoros
Africa
Active
USCFAFL
1
Western
Sahara
Africa
Eastern
Africa
Northern
Africa
Cease-fire,
often
broken
USCFAFL
1
Zimbabw
e
Africa
Southern
Africa
USCFAFL
1
Zambia
Africa
USCFAFL
1
AWB
South
Africa
Africa
Southern
Africa
Southern
Africa
USCFAFL
1
FDC
Angola
Africa
Southern
Africa
USCFAFL
1
FLEC
-FAC
FLEC
-R
UNIT
A
AFR
C
Angola
Africa
Active
USCFAFL
1
Angola
Africa
Active
USCFAFL
1
Angola
Africa
Active
USCFAFL
1
Sierra
Leone
Africa
Southern
Africa
Southern
Africa
Southern
Africa
Western
Africa
Ousted
USCFAFL
1
RUF
Sierra
Leone
Senegal
Africa
Cease-fire
2
Active
USCFAFL,
US
USCFAFL
Senegal
Africa
Active
USCFAFL
1
Mali
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Cease-fire,
3/1996
USCFAFL
1
FPLA
Mali
Africa
USCFAFL
1
Al Hadid
MPA
Mali
Africa
USCFAFL
1
Al Jihad
ARL
A
MFU
A
NPFL
Mali
Africa
USCFAFL
1
Mali
Africa
Cease-fire,
3/1996
Cease-fire,
3/1996
Cease-fire,
3/1996
Active
USCFAFL
1
Liberia
Africa
USCFAFL
1
ULI
MO
ULI
MO-J
Liberia
Africa
Disarming
(slowly)
cease-fire
USCFAFL
1
Liberia
Africa
cease-fire
USCFAFL
1
GuineaBissau
Africa
USCFAFL
1
All India Sikh
Students Federation
Khalistan
Commando Force
Khalistan
Liberation Front
Khalistan Zindabad
Force
Shan State Army,
or Shan State
Polisa
rio
MFD
C-FN
MFD
C-FS
FIAA
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Western
Africa
Active
1
181
Progress Army
Brethren
(Battalions) of the
Faithful
Internal Opposition
Zviadists
Lahkar-iJhangi
MAI
B
Equatorial
Guinea
Africa
Western
Africa
Nepal
Asia
Japan
Asia
Central
Asia
Eastern
Asia
Eastern
Asia
Popular Liberation
Army
ASG
Philippine
s
Asia
Ricardo Franco
Front
Anjouan Island
separatists
April 19 Movement
ABB
Philippine
s
Philippine
s
Philippine
s
Philippine
s
Philippine
s
Asia
NPA
MILF
Che Guevara
Brigade
Front for the
Restoration of
Unity and
Democracy
Front for the
Restoration of
Unity and
Democracy - Dini
National
Democratic Front
MNL
F
NDF
National
Democratic Front
of Hedayatollah
Matin-Daftari
National Front
National Liberation
Army of Iran
(Militant wing of
MEK)
National Resistance
Movement of Iran
Somalia
Democratic Front
Somalia Salvation
Democratic
Popular Front for
the Liberation of
Palestine
Hizballah External
Security
Organisation
Islamic Salvation
Front / Islamic
Salvation Army
Asia
Asia
Asia
Asia
Eastern
Asia
Eastern
Asia
Eastern
Asia
Eastern
Asia
Eastern
Asia
Active
USCFAFL
1
2
Active
USCFAFL,
US
USCFAFL,
EU, US
USCFAFL,
UK, AUS,
US
USCFAFL,
US
USCFAFL,
EU
USCFAFL
Cease-fire
USCFAFL
1
Active
USCFAFL
1
USCFAFL
1
Active,
training
terrorists
Active
USCFAFL,
US
2
USCFAFL
1
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Uncertain
USCFAFL
1
Active
Active
Active
3
4
2
2
1
Philippine
s
Asia
Eastern
Asia
JRA
Japan
Asia
Eastern
Asia
Sekih
otai
Japan
Asia
Eastern
Asia
Japan
Asia
Japan
Asia
Eastern
Asia
Eastern
Asia
Japan
Asia
China
Asia
China
Asia
FUL
RO
Vietnam
Asia
PUL
O
Thailand
Asia
Southeast
Asia
Dormant
USCFAFL
1
LTTE
Sri Lanka
Asia
Southeast
Asia
Active
USCFAFL,
UK, US
3
Eastern
Asia
Eastern
Asia
Eastern
Asia
Southeast
Asia
182
Cabinda
Democratic Front
South Ossetian
Rebels
Ukrainian SelfDefence
Organisation
White Legion
JVP
Sri Lanka
Asia
ARIF
Myanmar
Asia
KDA
Myanmar
Asia
KIA
Myanmar
Asia
Anti-Imperialist
Cell
June 2
DKB
A
KNU/
KNL
A
KA
Myanmar
Asia
Myanmar
Asia
Myanmar
Asia
MND
AA
NDA
A
NDA
Myanmar
Asia
Myanmar
Asia
Myanmar
Asia
RSO
Myanmar
Asia
SSA/
SSPA
Myanmar
Asia
Myanmar
Asia
SUR
A
UWS
A
LLA
Myanmar
Asia
Myanmar
Asia
Laos
Asia
LNL
M
ULN
LF
GPK,
FRET
ILIN
Laos
Asia
Laos
Asia
Indonesia
Asia
Indonesia
Asia
Southeast
Asia
Indonesia
Asia
Cambodia
Asia
India
Asia
India
Asia
India
Asia
Red Army Faction
Revolutionary Cells
Anarchist Street
Patrol
Children of
November
Conscientious
Arsonists
Fighting Guerrilla
Formation
Militant Guerilla
Formation
New Group of
Satanists
Revolutionary
Popular Struggle
Fadayan - Minority
Faction
Freedom
Movement of Iran
Iran Liberation
Front
United Front for the
Liberation of
Liberia
United Front for the
Liberation of
Liberia-Johnson
Fighting Islamic
Group in Libya
Islamic
Renaissance Party
Islamic Martyrs
Movement
Islamic Movement
for Change
Islamic Movement
of Martyrs
OPM
BK
Southeast
Asia
Southeast
Asia
Southeast
Asia
Active, but
limited
Active
USCFAFL
1
USCFAFL
1
Cease-fire
USCFAFL
1
Southeast
Asia
Southeast
Asia
Southeast
Asia
Cease-fire
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Cease-fire
USCFAFL
1
Cease-fire
USCFAFL
1
Cease-fire
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Cease-fire
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active;
talks
stalled
Active
USCFAFL
1
USCFAFL
1
Southeast
Asia
Southeast
Asia
Southern
Asia
Active
USCFAFL
1
Active
USCFAFL
1
Active
4
Southern
Asia
Southern
Asia
Active
USCFAFL,
EU, UK,
JPN
USCFAFL,
US
USCFAFL
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Southeast
Asia
Active
2
1
183
Libyan Jihad
Movement
Libyan National
Democratic
Movement
Libyan National
Grouping
Libyan National
Salvation
Committee
National Front for
the Salvation of
Libya
Macedonian
Revolutionary
Organisation Democratic Party
for Macedonian
National Unity
Unikom (ethnic
Albanians)
Azaouad IslamicArab Front
Azaouad Popular
Liberation Front
Azaouad Popular
Movement
Azaouad
Revolutionary
Army
United Azaoud
Movements and
Fronts
Justice Army of the
Defenseless People
Popular
Revolutionary
Army
Zapatista National
Liberation
Movement
Popular Front
Republic of
Transdniestr
Popular Front for
the Liberation of
Sakiet el Hamra
and Rio de Oro
Mozambican
National Resistance
National Resistance
Movement
Arakan Rohingya
Islamic Front
Southern
Asia
Southern
Asia
Active
USCFAFL
1
Active
USCFAFL
1
Southern
Asia
Southern
Asia
Active
USCFAFL
1
Active
USCFAFL
1
Asia
Southern
Asia
Active
politically
USCFAFL
1
India
Asia
Southern
Asia
Active
USCFAFL
1
BLTF
India
Asia
Active
USCFAFL
1
BSF
India
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Active
USCFAFL
1
India
Asia
Southern
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Southern
Asia
Southern
Asia
Active
USCFAFL
1
India
Asia
Southern
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
India
Asia
Active
USCFAFL
1
ATTF
HUA
JKLF
MCC
NDF
B
NLFT
India
Asia
India
Asia
India
Asia
India
Asia
India
Southern
Asia
Southern
Asia
Southern
Asia
184
Kachin Democratic
Army
Kachin
Independence
Army
Karen Buddhist
Democracy Army
Armenian
Liberation Army
Jaish e
Mohammend
NSC
N
PWG
India
Asia
India
Asia
ULF
A
India
Asia
Banglades
h
Tajikistan
Asia
Jeemah Islamiyah
Tajikistan
Asia
Ansar Al-Islam
Tajikistan
Asia
Lashkar I Jhangvi
Tajikistan
Asia
Kurdish
Communist Party
of Iran, Committee
of the
Revolutionary
Toilers of Iranian
Kordestan
Nadeem
Commando
Popular Front for
Armed Resistance
Shi'ite Movement
of Pakistan
Bougainville
Revolutionary
Army
Moro Islamic
Liberation Front
Moro National
Liberation Front
National
Democratic Front
Justice Commandos
of the Armenian
Genocide
People's
Combatants Group
Kyrgyzsta
n
Asia
Georgia
Asia
Georgia
Asia
Georgia
Asia
Georgia
Asia
Georgia
Asia
Georgia
Asia
Georgia
Asia
MLB
Bahrain
Asia
BRA
Papua
New
Guinea
Austria
Austria
Europe
Germany
Europe
Germany
Europe
National Liberation
Front of Kurdistan
People's Liberation
Army of Kurdistan
Harakat-ul-Ansar
Jamaat ul-Fuqra
IRP
UNS
O
BLA
VAP
O
AIZ
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Cease-fire
USCFAFL
1
Integrating
into
governmen
t
Active
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Western
Asia
Western
Asia
Western
Asia
Western
Asia
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
Western
Asia
Western
Asia
Western
Asia
Western
Asia
Cease-fire
USCFAFL
1
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Austral
ia
Australia
USCFAFL
1
Europe
Central
Europe
Central
Europe
Central
Europe
Central
Europe
Cease-fire
in effect:
4/30/98
Active
USCFAFL
1
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Southern
Asia
Western
Asia
Western
Asia
Western
Asia
Western
Asia
Western
Asia
Uncertain
185
Muhajir Quami
Movement - Haqiqi
Faction
Muttahidda Quami
Movement - Altaf
Faction
United Popular
Action MovementLautaro
Tibetan Separatists
RAF
Germany
Europe
Central
Europe
Disbanded
4/21/98
USCFAFL
1
RZ
Germany
Europe
Central
Europe
Dormant
USCFAFL
1
Russia
Europe
Eastern
Europe
Active
USCFAFL
1
Russia
Europe
USCFAFL
1
Guyana National
Service
Guyana People's
Militia
All Tripura Tiger
Force
PF
Moldova
Europe
USCFAFL
1
Moldova
Europe
USCFAFL
1
Macedoni
a,
FYROM
Europe
Eastern
Europe
Eastern
Europe
Eastern
Europe
Southeast
Europe
USCFAFL
1
Macedoni
a,
FYROM
Greece
Europe
Southeast
Europe
Active
USCFAFL
1
Europe
Southeast
Europe
Active
USCFAFL,
EU, UK, US
4
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Southeast
Europe
Southeast
Europe
Southeast
Europe
Southeast
Europe
Southeast
Europe
Active
USCFAFL
1
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Active
USCFAFL
1
Greece
Europe
Southeast
Europe
Southeast
Europe
Southeast
Europe
Active
USCFAFL
1
Italy
Europe
Dormant
Italy
Europe
Active
USCFAFL,
US
USCFAFL
2
Tudeh
Al-Dawa alIslamiya
Communist Party
militia
Cabinda Enclave
Liberation Front Cabinda Armed
Italy
Europe
Active
USCFAFL
1
Italy
Europe
Active
USCFAFL
1
Spain
Europe
Southern
Europe
Southern
Europe
Southern
Europe
Southern
Europe
Southwest
Europe
Active
USCFAFL,
EU, UK, US
4
VMR
ODPM
N
Ananda Marg
Shan United
Revolutionary
Army (Mong Tai
Army)
United Wa State
Army
Revolutionary
Armed Front
Azaouad Liberation
Front
Organisation de la
Resistance
Revolutionary
Liberation Army of
North-Niger
Saharan Patriotic
Liberation Front
Baluch People's
Liberation Front
Baluch Students'
Organization Awami
Paykar
RO17
MAS
ELA
BR
ETA
1
186
Forces
Cabinda Enclave
Liberation Front Renovada
National Union for
the Total
Independence of
Angola
Bavarian Liberation
Army
Uighur Muslim
Separatists
April 19 Movement
Peasant SelfDefense Group of
Cordoba and Uraba
Loyalist Volunteer
Force
Ulster Defense
Association
Ulster Freedom
Fighters
International Sikh
Youth Federation
Islamic Army of
Aden
Islamic Movement
of Uzbekistan
National Liberation
Army - Colombia
Revolutionary
Proletarian Army
Azorean Liberation
Front
Azorean Nationalist
Movement
Popular Forces of
the 25th of April
Islamic Tunisian
Front
Srpska
Dobrovoljacka
Garda (SDG)
Srpska Garda
Real IRA
Red Hand
Defenders
Revolutionary
Nuclei
Asbat Al-Ansar
GRA
PO
Spain
Europe
Southwest
Europe
Dormant
USCFAFL,
EU, US
3
Spain
Europe
Southwest
Europe
Inactive
USCFAFL
1
Spain
Europe
Uncertain
USCFAFL
1
Portugal
Europe
USCFAFL
1
Portugal
Europe
USCFAFL
1
FP-25
Portugal
Europe
Southwest
Europe
Southwest
Europe
Southwest
Europe
Southwest
Europe
USCFAFL
1
CIRA
United
Kingdom
United
Kingdom
United
Kingdom
United
Kingdom
United
Kingdom
United
Kingdom
United
Kingdom
France
Europe
Active
3
Active
USCFAFL,
EU, US
USCFAFL,
EU, US
USCFAFL,
EU, US
USCFAFL,
US
USCFAFL
Active
USCFAFL
1
Cease-fire
USCFAFL
1
Dormant
USCFAFL
1
FLN
CHW
FLN
C-CH
FLN
KS
France
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Western
Europe
Active
USCFAFL
1
France
Europe
Active
USCFAFL
1
France
Europe
USCFAFL
1
CCC
Belgium
Europe
USCFAFL
1
Iraq
Middle
East
Western
Europe
Middle
East
Active,
nonviolent
Dormant
AUS, US
2
Tunisia
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
LVF
UDA
IRA
CAC
INLA
UVF
AD
Tunisia
Tunisia
FIT
Tunisia
Europe
Europe
Europe
Europe
Europe
Europe
Europe
Western
Europe
Western
Europe
Active
Cease-fire
Active
3
3
2
1
187
Ansuman¨¦ Man¨¦
rebellion
Azar Khalistan
Babbar Khalsa
Force
Bodo Liberation
Tiger Force
Bodo Security
Force
Dal Khalsa
Polisa
rio
FIGL
Morocco
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
Egypt
Middle
East
Middle
East
4
Egypt
Middle
East
Middle
East
Middle
East
Middle
East
USCFAFL,
UK, AUS,
US
USCFAFL
USCFAFL
1
Egypt
Middle
East
Middle
East
USCFAFL
1
GIA
Algeria
Algeria
Middle
East
Middle
East
Middle
East
Middle
East
USCFAFL,
UK, US
USCFAFL
3
AKA
L
Middle
East
Middle
East
Middle
East
Middle
East
USCFAFL
1
Active
USCFAFL
1
Yemen
Middle
East
Middle
East
Active
USCFAFL
1
DHK
P/C,
Dev
Sol
PKK
Turkey
Middle
East
Middle
East
Active
USCFAFL,
EU, UK,
JPN, US
5
Turkey
USCFAFL,
EU
USCFAFL
2
Turkey
Middle
East
Middle
East
Active
ALA
Middle
East
Middle
East
Libya
Libya
Libya
Libya
Dashmesh
Regiment
Garo National
Front
Harakat ul-Ansar
Libya
Jamaat-e-Islam
Libya
Jammu and
Kashmir Liberation
Front
Hezbollah Gulf
Libya
Islamic Jihad in
Hejaz
Islamic Peninsula
Movement for
Change - Jihad
Wing
Islamic
Revolutionary
Organization
Irish National
Liberation Army
Red Hand
Commandos
Ulster Volunteer
Force
Armed
Commandos for
National Liberation
Palestine Islamic
Jihad - Shiqaqi
faction
United People's
Front of Nepal
Sipah-i-Sahaba
Pakistan
Tupac Amaru
Revolutionary
Movement
Libya
Libya
EIJ
IG /
GAI
Egypt
Algeria
FIS/A
IS
Algeria
Active
Active
Active
Uncertain
Dormant
1
1
1
188
Alex Boncayo
Brigade
New People's Army
ASA
LA
Turkey
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Dormant
USCFAFL
1
Active
USCFAFL
1
Kurdistan Workers
Party
Allied Democratic
Forces
Lord's Resistance
Army
Irish Republican
Army
Eastern Turkistan
Islamic Movement
Harkat-i-Islami
JCAG
Turkey
Dormant
USCFAFL
1
ERN
K
ARG
K
TYK
B
Turkey
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active, but
suppressed
Active
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Saudi
Arabia
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Saudi
Arabia
Middle
East
Middle
East
Active
USCFAFL
1
Saudi
Arabia
Middle
East
Middle
East
Active
USCFAFL
1
Saudi
Arabia
Saudi
Arabia
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Saudi
Arabia
Saudi
Arabia
Saudi
Arabia
Saudi
Middle
East
Middle
East
Middle
East
Middle
Middle
East
Middle
East
Middle
East
Middle
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Turkey
Turkey
Turkey
Syria
Sudan
Jamaat e Islami
Sudan
National Islamic
Movement
Northern Alliance
Sudan
NDA
Sudan
Taliban Militia
SSIM
Sudan
United Islamic
Front for the
Salvation of
Afghanistan
Alliance for a Free
Kabylie
Belmokhtar Group
SAF
Sudan
SPLA
Sudan
Movement for
Democracy and
Development
National
Awakening
Committee for
Peace and
Democracy
National Council
for Rebuilding
Chad
National Council
for Recovery
National Front for
the Renewal of
Chad
People's
Democratic Front
Union of
Democratic Forces
Lautaro Youth
Movement
Manuel Rodriguez
Sudan
189
Patriotic Front Autonomous
Manuel Rodriguez
Patriotic Front Dissidents
Red Sun
Arabia
East
East
Saudi
Arabia
Middle
East
Middle
East
Active
USCFAFL
1
SSP
Pakistan
Pakistan
Unclear
USCFAFL,
US
USCFAFL
2
BPLF
Islamic Group
BSOA
Pakistan
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
al-Jihad
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Unclear
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Vanguards of
Conquest
Farabundo Marti
National Liberation
Front
Movement for the
Autodetermination
of the Island of
Bioko
Eritrean Liberation
Front
Al-Ittihad al-Islami
MQM
-H
Pakistan
Middle
East
Middle
East
Active
USCFAFL
1
MQM
Pakistan
USCFAFL
1
Active
USCFAFL
1
PFAR
Middle
East
Middle
East
Middle
East
Middle
East
Active
Action Directe
Middle
East
Middle
East
Middle
East
Middle
East
Unclear
USCFAFL
1
Active
USCFAFL
1
Lebanon
Middle
East
Middle
East
Dormant
5
Lebanon
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL,
EU, UK,
JPN, US
USCFAFL
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
Middle
East
Middle
East
Middle
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
Corsican National
Liberation FrontHistoric Wing
Maoist Communist
Center
Muslim
Brotherhood
National
Democratic Front
of Bodoland
National Liberation
Front of Tripura
National Socialist
Council of
Nagaland
People's War
Group
United Liberation
Front of Assam
Revolutionary
Front for an
Independent East
Timor
Gerakin Aceh
Merdeka
Organisasi Papua
Merdek
Al-Harakan al-
Pakistan
1
Pakistan
Pakistan
Pakistan
Pakistan
PKK
Lebanon
Lebanon
Lebanon
Lebanon
Lebanon
Lebanon
Lebanon
Lebanon
MUI
Lebanon
Active
1
190
Islamiya
Ansar-e Hezbollah
East
Middle
East
Middle
East
Middle
East
Middle
East
East
Middle
East
Middle
East
Middle
East
Middle
East
Lebanon
Middle
East
Lebanon
Lebanon
Active
USCFAFL
1
Dormant
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Israel
Middle
East
Middle
East
Active
5
Israel
Middle
East
Middle
East
Active
USCFAFL,
EU, UK,
JPN, US
USCFAFL,
EU, UK,
AUS, US
USCFAFL,
EU, JPN,
US
USCFAFL,
EU, UK, US
USCFAFL,
EU, JPN,
US
USCFAFL,
EU, JPN,
US
USCFAFL,
EU, JPN,
US
Babak Khoramdin
Organisation
Banner of Kaveh
FAR
L
Amal
Lebanon
Democratic Party
of Iranian
Kurdistan
Democratic
Revolutionary
Front for the
Liberation of
Arabistan
Fadayan - Majority
Faction
Kurdish
Democratic Party
of Iran
Iranian Democratic
Party of Kurdistan
PFLP
Lebanon
Lebanon
Jordan
ANO
Islamic Movement
of Kurdistan
5
Kurdistan
Democratic Party
Kach
Israel
Middle
East
Middle
East
Active
Patriotic Union of
Kurdistan
Socialist Party
militia
PIJ
Israel
Israel
Middle
East
Middle
East
Active
PLF
Middle
East
Middle
East
Supreme Council
for Islamic
Revolution
Supreme Council
for the Islamic
Resistance in Iraq,
Badr Corps
Turcoman Front
Militia
Democratic Front
for the Liberation
of Palestine
Fatah Uprising
PFLP
Israel
Middle
East
Middle
East
Active
PFLP
-GC
Israel
Middle
East
Middle
East
Active
DFLP
Israel
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
Middle
East
Middle
East
Middle
East
Middle
East
Middle
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Inactive
USCFAFL
1
Active
USCFAFL
1
Israel
Israel
Gush Emunim
Underground
Islamic Jihad
May 15
Organization
Organization of the
Israel
Israel
OAA
S
PFLP
Israel
Israel
Active, but
restrained
4
4
4
4
4
191
Armed Arab
Struggle
Popular Front for
the Liberation of
Palestine-Special
Command
Popular Struggle
Front
Terror Against
Terror
Autonomists
-SC
Hammer Skinheads
Italia
Third Position
KDPI
Iraq
IMK
Iraq
KDP
Iraq
PUK
Iraq
Blood Revenge
Corps of the
Partisan Volunteer
Corps for the
Independence of
the Japanese Race
Kakamaru-ha
Middle Core
Faction, or Nucleus
Sane Thinkers
School
Jordanian Muslim
Brotherhood
Independence
Lao Liberation
Army
Lao National
Liberation
Movement
United Lao
National Liberation
Front
Al Ekhouwan al
Muslimin
Al Gamaat Al
Islamiyya
Al Taqfeer Wal
Hijra
Army of Palestine
East
East
PSF
Israel
Middle
East
Middle
East
TNT
Israel
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
Active
USCFAFL,
EU, UK,
JPN, US
5
Iran
Middle
East
Middle
East
Uncertain
USCFAFL
1
Iran
Middle
East
Middle
East
Active
USCFAFL
1
Iran
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Uncertain
USCFAFL
1
USCFAFL
1
USCFAFL
1
Iraq
Iraq
Iraq
Iraq
SCIR
I
Iraq
Iraq
MEK/
MKO
,
PMOI
,
NLA
BKO
Iran
Iran
DPIK
Iran
Iran
Fatah
Iran
Hamas
Iran
Dormant
Active
Active
192
Hezbollah
FMI
Islamic Jehad
Islamic Resistance
Islamic Unification
Movement
Jamaat Al Noor
Lebanese Armed
Revolutionary
Faction
Lebanese
Resistance
Detachments
Popular Front for
the Liberation of
Palestine
Usbat Al Ansar
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Iran
Middle
East
Iran
Iran
Iran
Iran
KOM
ALA
KDP
Iran
Iran
Iran
Iran
NLA
Usbat Al Nour
Iran
Iran
National Patriotic
Front of Liberia
Armenian Secret
Army for the
Liberation of
Armenia
Grey Wolves
(Idealists)
Armed Forces of
National Liberation
Armed Forces of
Popular Resistance
Army of God
Iran
Bahrain
IFLB
NIM
Aryan Nations
Guerrilla Forces of
Liberation
Los Macheteros
UIFS
A
Hamas
Kurdistan Workers
Party
Revolutionary
People's Liberation
Party/Front
Egyptian Islamic
Jihad Group
U?K/
KLA
FAR
K
LAK
Bahrain
Afghanist
an
Afghanist
an
Afghanist
an
Afghanist
an
Afghanist
an
Afghanist
an
Yugoslavi
a
Turkey
Yugoslavi
a
Egypt
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Middle
East
Active
USCFAFL
1
Middle
East
Middle
East
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL
1
Active
USCFAFL
1
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Active
USCFAFL
1
Uncertain
USCFAFL
1
Active
USCFAFL
1
USCFAFL
1
Middle
East
Middle
East
USCFAFL
1
193
November 17
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
NA
Middle
East
Middle
East
Middle
East
Middle
East
Middle
East
NA
NA
NA
NA
NA
NA
NA
EU, JPN,
US
3
ISYF
NA
NA
EU, UK,
JPN
3
UK, AUS,
US
UK, AUS,
US
3
Babbar Khalsa
Kahane Chai
Al-Aqsa Martyrs
Brigade
Salafist Group for
Call and Combat
Militia Groups
UFF
Yugoslavi
a
Yugoslavi
a
Yugoslavi
a
Israel
Algeria
IG
Cease-fire
Cease-fire
USCFAFL
1
USCFAFL
1
USCFAFL
1
USCFAFL,
EU, US
USCFAFL
3
EU, UK,
JPN, US
EU, UK,
AUS, US
UK, AUS,
US, JPN
4
1
Mountaineer
Militia
Organization of
Volunteers for the
Puerto Rican
Revolution
People's
Revolutionary
Commandos
National Liberation
Movement
(Tupamaros)
Red Flag
LET,
LT
IAA
NA
NA
United
Revolutionary
Front
United Front for the
Liberation of
Oppressed Races
Popular Front for
the Liberation of
Sakiet el Hamra
and Rio de Oro
Yemeni Tribesmen
Beli Orlovi
Kosovo Liberation
Army
Kosovo Republic
Armed Forces
Liberation Army of
Kosova
National Movement
for the Liberation
of Kosovo
Srpski Cetnicki
Pokret
Black Mamba
Chimwenje
Al-Takfir and AlHijra
Great Islamic
Eastern Warriors
IMU
NA
NA
GSPC
NA
NA
UK, AUS,
US
3
RIRA
NA
NA
EU, US
2
RHD
NA
NA
NA
NA
NA
NA
EU, US
EU, US
UK, AUS
2
2
2
NA
NA
UK, AUS
2
JeM
NA
NA
UK, US
2
JI
NA
NA
UK, US
2
LJ
NA
NA
AUS, US
2
ETIM
NA
NA
NA
NA
NA
NA
CHN, US
USCFAFL
EU
2
1
1
NA
NA
EU
1
IBDA
-C
Middle
East
4
4
3
194
Front
Holy Land
Foundation for
Relief and
Development
Stichting Al Aqsa
Orange Volunteers
Revolutionary
Popular Struggle
Harakat
Mujahideen
Harakat UlMujahideen
Jaish-iMohammend
Jemaah Islamiyah
The Eastern
Turkistan
Liberation
Organization
The World Uighur
Youth Congress
Communist Party
of Philippines/New
People's Army
Harakat ulMujahidin
Hizballah
Al-Badhr
Mujahedin
Anti-Imperialist
Territorial Nuclei
Army for the
Liberation of
Rwanda
Cambodian
Freedom Fighters
Harakat ul-Jihad-IIslami
Harakat ul-Jihad-IIslami/Bangladesh
Hizb-I Islami
Gulbuddin
Islamic
International
Peacekeeping
Brigade
Jamiat ulMujahedin
Kumpulan
Mujahidin
Malaysia
Libyan Islamic
Fighting Group
NA
NA
EU
1
OV
ELA
NA
NA
NA
NA
NA
NA
EU
EU
EU
1
1
1
HM
NA
NA
UK
1
NA
NA
AUS
1
NA
NA
AUS
1
NA
NA
NA
NA
AUS
CHN
1
1
WUY
C
CPP?
NPA
NA
NA
CHN
1
NA
NA
US
1
HUM
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
NTA
NA
NA
US
1
ALIR
NA
NA
US
1
CFF
NA
NA
US
1
HUJI
NA
NA
US
1
HUJIB
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
ETL
O
Middle
East
KMM
195
Moroccan Islamic
Combatant Group
New Red
Brigade/Communis
t Combatant Party
People Against
Gangsterism and
Drugs
Riyadus-Salikhin
Reconnaissance
and Sabotage
Battalion of
Chechen Martyrs
Special Purpose
Islamic Regiment
The Tunisian
Combatant Group
Turkish Hizballah
The East Turkistan
Information Center
Revolutionary
Proletarian
Initiative Nuclei
Basque Fatherland
and Liberty
GIC
M
BR/P
CC
NA
NA
US
1
NA
NA
US
1
PAG
AD
NA
NA
US
1
NA
NA
US
1
NA
NA
US
1
TCG
NA
NA
US
1
ETIC
NA
NA
NA
NA
US
CHN
1
1
NIPR
NA
NA
US
North
America
Active;
low level
USCFAFL
1
Gama'a alIslamiyya
FAL
N
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
a
North
Americ
North
America
Active;
low level
USCFAFL
1
North
America
Active;
low level
USCFAFL
1
North
America
Active
USCFAFL
1
North
America
Active
USCFAFL
1
North
America
Active;
low level
USCFAFL
1
North
America
Active;
low level
USCFAFL
1
North
America
Active
USCFAFL
1
North
America
Active
USCFAFL
1
North
America
Active;
low level
USCFAFL
1
North
America
Active;
low level
USCFAFL
1
United
States
Lashkar e
Tayyaba/Pashan-eAhle Hadis
Al Qaida
Armed Islamic
Group
Revolutionary
Armed Forces of
Colombia
United SelfDefense Forces of
Colombia
Aum Shinrikyo
United
States
United
States
United
States
AN
United
States
United
States
United
States
United
States
October First AntiFascist Resistance
Group
Liberation Tigers
of Tamil Eelam
United
States
Continuity Irish
Republic Army
United
States
United
States
196
Anti-Imperialist
Patriotic Union
SL
Peru
Alfaro Lives,
Damnit!
MRT
A
Peru
Syrian Muslim
Brotherhood
FLQ
Canada
Tigers of the Gulf
UPA
Popular Front for
the Liberation of
Palestine-General
Command
Shining Path
BR
Dominica
n
Republic
Middle
East
Abu Sayyaf Group
MLN
Uruguay
Horsemen
FAR
Nicaragua
Cinchonero Popular
Liberation
Movement
Comandos
Operativos
Especiales
Morazanist
Patriotic Front
Venezuel
a
Mexico
EPR
Mexico
EZL
N
Mexico
Karen National
Union / Karen
National Liberation
Army
Karenni Army
MPL
Honduras
COE
S
Honduras
Myanmar National
Democratic
Alliance Army
National
Democratic
Alliance Army
New Democratic
Army
FPM
Honduras
FRPLZ
Honduras
Rohingya
Solidarity
Organization
Shan State
Guyana
Guyana
URN
Guatemal
a
North
Americ
a
North
Americ
a
North
Americ
a
South
Americ
a
South
Americ
a
North
America
Active
North
America
Active
North
America
Inactive
South
America
USCFAFL,
EU, JPN,
US
USCFAFL,
US
4
2
USCFAFL
1
USCFAFL
1
South
America
Uncertain
USCFAFL
1
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
America
Uncertain
USCFAFL
1
South
America
Legal
political
party
Active
USCFAFL
1
USCFAFL
1
South
America
Active
USCFAFL
1
South
America
Active
USCFAFL
1
South
America
Active,
making
peace
Unclear;
long quiet
USCFAFL
1
USCFAFL
1
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
South
America
Active
USCFAFL
1
South
America
Inactive,
but intact
USCFAFL
1
South
America
Inactive
USCFAFL
1
South
America
Disbanded
USCFAFL
1
South
America
Disbanded
USCFAFL
1
South
Disarmed
USCFAFL
1
South
America
South
America
197
Restoration Council
(Mong Tai Army)
Fighting Ansar of
Allah
G
a
FML
N
El
Salvador
Jamaat al-Adala alAlamiya
AVC
Ecuador
Legion of the
Martyr Abdullah
al-Huzaifi
Movement for
Islamic Change
GCP
Ecuador
Armed Forces
Revolutionary
Council
Revolutionary
United Front
M-19
Rahanwein
Resistance Army
FAR
C
Colombia
Somali Democratic
Alliance
AUC
Colombia
Somali Democratic
Association
ELN
Colombia
Somali National
Alliance
M-19
Colombia
Somali National
Front
ACC
U
Colombia
Somali National
Movement
EPL
Colombia
Somali Patriotic
Movement
FRF
Colombia
United Somali
Congress
MJL
Chile
United Somali
Front
FPM
R/A
Chile
Afrikaaner
Weestand
Beweeging & Boer
Attack Troops
Iraultza
FPM
R/D
Chile
MIR
Chile
Those of the North
MAP
Chile
Ecuador
Cuba
Cuba
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
Americ
a
South
America
in 1997
South
America
Political
Party
USCFAFL
1
South
America
USCFAFL
1
South
America
Active
(one
faction)
Formed in
11/1998
USCFAFL
1
South
America
Officially
disbanded
USCFAFL
1
South
America
Inactive
USCFAFL
1
South
America
Inactive
USCFAFL
1
South
America
Active
USCFAFL,
EU, US
3
South
America
Active
USCFAFL,
EU, US
3
South
America
Active
USCFAFL,
US
2
South
America
A few are
active
USCFAFL
1
South
America
Active
USCFAFL
1
South
America
Active
USCFAFL
1
South
America
Active (as
bandits)
USCFAFL
1
South
America
Dormant
USCFAFL
1
USCFAFL
1
South
America
South
America
Active
(Chile's
only)
USCFAFL
1
South
America
Dormant
USCFAFL
1
USCFAFL
1
South
198
U-L
Americ America
a
Islamic Liberation
ELN
Bolivia
South
South
Dormant,
USCFAFL
1
Party
Americ America
1996
a
Islamic Tendency
EGT
Bolivia
South
South
Possibly
USCFAFL
1
Party
K
Americ America
active
a
Sources: USCFAFL = United States Committee For A Free Lebanon; US = United State Dept. Report;
UK = Uinited Kingdom Government; EU = European Union; AUS = Australia Government; CHN =
PRC Government; JPN = Japan Government
URL
http://1osamabinladen.5u.com/
http://daawaparty.com/
http://impact.users.netlink.co.uk/namir/
namirm.html
http://jorgevinhedo.sites.uol.com.br/in
dex.html
http://www.abrarway.com/
http://www.alfida.jeeran.com/
http://www.alokab.com/
http://www.alsakifah.org/
http://www.ansar.ws
http://www.cihad.net/
http://www.clearguidance.com/
http://www.dhkc.info/
http://www.dhkc.net/
http://www.etehadefedaian.org/
http://www.expliciet.nl/
http://www.ezzedeen.net/
http://www.fadai.org/
http://www.fadaian.org/
http://www.h-alali.net/
http://www.hilafet.com/
http://www.hizbollah.tv/
http://www.hizb-ut-tahrir.org/
http://www.infopalestina.com/
http://www.intiqad.com/
http://www.iran.mojahedin.org/
http://www.iran-e-azad.org/
http://www.iranncrfac.org/
http://www.islamicdawaparty.org/
http://www.israel-wat.com/
http://www.jihadunspun.com/
http://www.kahane.org/
http://www.kavkazcenter.net/
http://www.kdp.info/
http://www.kdp.pp.se/
http://www.kdp.pp.se/
Group Name
Al-Qaeda
Islamic Dawa Party
National Movement of Iranian Resistance
Location
Unknown
Iraq
Iran
Markaz Ad-Dawa Wal Irshad
Pakistan
Islamic Jihad of Palestine
Al-Qaeda
Salafi Group
Salafi Group
Al-Ansar
Salafi
Salafi group
Revolutionary Peoples Liberation Front
Revolutionary Peoples Liberation Front
The Union Of People's Fedaian Of Iran
Hizb-ut Tahrir
Hamas
The Organization of Iranian People's
Fedaian (Majority)
The Organization of Iranian People's
Fedaian (Majority)
Salafi Group
Hizb-ut Tahrir
Hizballah
Hizb-ut Tahrir
Hamas
Hizballah
People's Mojahedin of Iran
National Council of Resistance of Iran
National Council of Resistance of Iran
Islamic Dawa Party
Unknown
Salafi Group
Kahane Chai
Chechen Rebels
Kurdistan Democratic Party-Iraq
Kurdistan Democratic Party
Kurdistan Democratic Party-Iraq
Palestine
Unkown
Unknown
Unknown
Unknown
Unknown
Unkown
Turkey
Turkey
Iran
Unknown
Palestine
Iran
Iran
Unknown
Unknown
Lebanon
Unknown
Palestine
Lebanon
Iran
Iran
Iran
Iraq
Unknown
Unknown
Israel
Russia
Iraq
Iraq
Iraq
199
http://www.khilafah.com/
http://www.kurdistanmedia.com/
http://www.maktab-al-jihad.com/
http://www.manartv.org/
http://www.moqawama.tv/
http://www.nasrollah.org/
http://www.nehzateazadi.org/
http://www.pdk-iran.org/
http://www.puk.org/
http://www.qoqaz.com/
http://www.qudsway.com/
http://www.rantisi.net/
http://www.sahwah.com/
http://www.sciri.btinternet.co.uk/
http://www.shareeah.org/
http://www.siahkal.com/
http://www.specialforce.net/
http://www.tanzeem.org/
http://www.tawhed.ws/
http://www.tudehpartyiran.org/
http://www.ummahnews.com/
http://www.wpiran.org/
http://www.wsahara.net/
http://download.specialforce.net
http://palestine-info-urdu.com
http://web.manartv.org
http://www.abrarway.com
http://www.al-fateh.net
http://www.alokab.com
http://www.alsakifah.org
http://www.cihad.net
http://www.clearguidance.com
http://www.expliciet.nl
http://www.ezzedeen.net
http://www.h-alali.net
http://www.hilafet.com
http://www.hizbollah.tv
http://www.hizb-ut-tahrir.org
http://www.infopalestina.com
http://www.intiqad.com
http://www.jihadunspun.com
http://www.kataebalaqsa.com
http://www.kavkazcenter.net
http://www.khilafah.com
http://www.maktab-al-jihad.com
http://www.moqawama.tv
http://www.nasrollah.org
http://www.qoqaz.com
http://www.qudsway.com
http://www.rantisi.net
http://www.sahwah.com
http://www.shareeah.org
http://www.tanzeem.org
Hizb-ut Tahrir
Democratic Party of Iranian Kurdistan
Salafi Group
Hizballah
Hizballah
Hizballah
Freedom Movement of Iran
Democratic Party of Iranian Kurdistan
Patriotic Union Of Kurdistan
Chechen Rebels
Islamic Jihad
Hamas
Salafi Group
The Supreme Council for the Islamic
Revolution in Iraq
Al-Qaeda
The Iranian People's Fadaee Guerrillas
Hizballah
Tanzeem Islami
Al-Qaeda
Tudeh Party
Salafi Group
Worker-Communist Party of Iran
Polisario
Hizballah
Hamas
Hizballah
Islamic Jihad
Hamas
Salafi Group
Salafi Group
Salafi
Salafi group
Hizb-ut Tahrir
Hamas
Salafi Group
Hizb-ut Tahrir
Hizballah
Hizb-ut Tahrir
Hamas
Hizballah
Salafi Group
Al-Aqsa Martyrs Brigade
Chechen Rebels
Hizb-ut Tahrir
Salafi Group
Hizballah
Hizballah
Chechen Rebels
Islamic Jihad
Hamas
Salafi Group
Al-Qaeda
Tanzeem Islami
Unknown
Iran
Unknown
Lebanon
Lebanon
Lebanon
Iran
Iran
Iraq
Russia
Palestine
Palestine
Unknown
Iraq
Unkown
Iran
Lebanon
Pakistan
Unknown
Iran
Unknown
Iran
Morocco
Lebanon
Palestine
Lebanon
Palestine
Palestine
Unknown
Unknown
Unknown
Unkown
Unknown
Palestine
Unknown
Unknown
Lebanon
Unknown
Palestine
Lebanon
Unknown
Plalestine
Russia
Unknown
Unknown
Lebanon
Lebanon
Russia
Palestine
Palestine
Unknown
Unkown
Pakistan
200
http://www.ummahnews.com
http://arabhackerz.8m.com/
http://wps.jeeran.com
http://www.muhajiroun.com/
http://www.almaqreze.com/
http://www.islammemo.cc/
http://www.ansar-sonnah.8m.com/
http://www.geocities.com/salafiahweb/
http://www.muslman.com/
http://www.freewebs.com/abuomar/
http://www.qal3ati.net/vb/
http://www.ajnad.50megs.com/
http://www.qawem.org
http://www.kataebq.com
http://www.sabiroon.org
http://www.palestine-info.info
http://www.gamla.org.il
http://audio.kavkazcenter.com
http://old.kavkazcenter.com
http://www.kavkaz.tv
http://www.kavkaz.uk.com
http://www.kavkaz.org.uk
http://www.kavkazcenter.com
http://www.kavkazcenter.info
http://www.chechen.org
http://www.chechnya.biz
http://ichkeria.dk
http://serlo.prv.pl
http://www.chechnya.250x.com
http://www.ichkeria.250x.com
http://www.takbir.by.ru
http://vaynahchat.com
http://www.shamilonline.tk
http://www.kavkazchat.com
http://www.zurho.tk
http://www.zamir-j.de
http://www.al-nahda.org
http://www.ummah.ru
http://al-halifat.narod.ru
http://jihadfilislam.by.ru
http://www.salam-alejkum.narod.ru
http://amir-mumin.boom.ru
http://ahlbayt.by.ru
http://www.bedir.ru
http://www.tawhid.narod.ru
http://www.daymohk.info
http://manhaj.narod.ru
http://www.peshera.pochta.ru
http://www.ramzan.kazan.ws
http://nsudenko.narod.ru
http://www.engenoi.kazan.ws
http://www.al-halifat.narod.ru
http://www.jiubila.com
Salafi Group
Unknown
Hamas
Salafi Group
Salafi Group
Salafi Group
Salafi Group
Salafi Group For Call and Combat
Salafi Group
Salafi Group
Salafi Group
Salafi Group
Hamas
Qassam Brigade-Military wing of Hamas
Hamas
Hamas
Supporters of Jewish Extremists
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Unknown
Unknown
Palestine
Unknown
Saudi Arabia
Unknown
Iraq
Algeria
Unknown
Unknown
Unknown
Iraq
Palestine
Palestine
Palestine
Palestine
Israel
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
201
http://www.zabarsh.nm.ru
http://chechenstar.narod.ru
http://www.resistance.by.ru
http://www.ichkeriya2004.narod.ru
http://www.gazavat.narod.ru
http://www.bachi-yurt.tk
http://almatinec.boxmail.biz
http://pyti-yti.narod.ru
http://www.daimokh.narod.ru
http://www.vaynah.com
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Chechen Rebels
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Russia
202
APPENDIX C
US DOMESTIC EXTREMIST FORUMS
Category
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Neo-Nazis
Group
Name
National
Alliance
National
Alliance
National
Alliance
National
Socialist
Movement
National
Socialist
Movement
National
Socialist
Movement
National
Socialist
Movement
National
Socialist
Movement
National
Socialist
Movement
National
Forum Name
Forum
URL
Founder
Found Date
American
National
Socialist
Group
Klandestine
Knights
Resistencia
Aria
NSM World
Yahoo
http://groups.yahoo.com/group/amer
icannationalsocialistgroup/
[email protected]
Nov 26, 2003
MSN
http://groups.msn.com/Klandestine
Knights/home.htm
http://groups.msn.com/ResistenciaA
ria/home.htm
http://groups.yahoo.com/group/nsm
world/
RButler64
MSN
Yahoo
talk.politics.th
eory
Google
alt.revisionism
Google
alt.politics.whi
te-power
Google
alt.politics.nati
onalism.white
Google
alt.conspiracy
Google
National
MSN
http://groups.google.com.mx/groups
?hl=es&lr=&group=talk.politics.the
ory
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.revisionism
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.white-power
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.nationalism.white
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.conspiracy
http://groups.msn.com/NationalSoci
[email protected]
Jan 12, 2004
203
Neo-Nazis
Socialist
Movement
N/A
Socialist
Movement
Neo-Nazi
alistMovement/home.htm
Yahoo
Neo-Nazis
N/A
Angelic_Adolf
Yahoo
Neo-Nazis
N/A
africanh8ters
Yahoo
Neo-Nazis
N/A
naziwoman
Yahoo
Neo-Nazis
N/A
smashnazism
Yahoo
Neo-Nazis
N/A
Yahoo
Neo-Nazis
N/A
Neo-Nazis
N/A
Neo-Nazis
N/A
thejapanesenaz
is
ilovewhitefolk
s3
INVISIBLE_E
MPIRE
misc.activism.
militia
Neo-Nazis
N/A
alt.politics.nati
onalism.white
Google
Neo-Nazis
N/A
misc.activism.
progressive
Google
Neo-Nazis
N/A
sci.skeptic
Google
Neo-Nazis
N/A
alt.politics.whi
te-power
Google
Neo-Nazis
N/A
alt.skinheads
Google
Yahoo
Yahoo
Google
http://groups.yahoo.com/group/NeoNazi/
http://groups.yahoo.com/group/Ang
elic_Adolf/
http://groups.yahoo.com/group/afric
anh8ters88/
http://groups.yahoo.com/group/nazi
woman/
http://groups.yahoo.com/group/sma
shnazism/messages/81
http://groups.yahoo.com/group/theja
panesenazis/
http://groups.yahoo.com/group/ilov
ewhitefolks3/
http://groups.yahoo.com/group/INV
ISIBLE_EMPIRE
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.militia
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.nationalism.white
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=sci.ske
ptic
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.white-power
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.skin
heads
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Jan 14, 2004
Dec 10, 2002
Jan 7, 2003
Dec 14, 2001
Jan 26, 2002
May 12, 2003
Jul 23, 2002
Jan 18, 2002
204
Neo-Nazis
N/A
nazis of 2004
AOL
Neo-Nazis
N/A
Aryan Raiders
MSN
Neo-Nazis
Neo-Nazis
N/A
Women for
Aryan Unity
American
Nazi Party
American
Nazi Party
American
Nazi Party
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
NEONAZI
Women for
Aryan Unity
American Nazi
Party
American Nazi
Party
American Nazi
Party
World_Knight
s
MSN
US
WEB
US
WEB
US
WEB
US
WEB
Yahoo
aryannationskn
ights
Neo-Nazis
Neo-Nazis
Neo-Nazis
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
http://groups.aol.com/sknhdsn?mmc
h_=0
http://groups.msn.com/AryanRaider
s/
http://groups.msn.com/NEONAZI/
http://www.stormfront.org/
KORNvsSLIPKNOT1
ARFounder
Feb. 11 2004
X Seether X
http://www.nazi.org/current/forum/
http://www.nationalist.org/forum/in
dex.php
http://www.whiterevolution.com/for
um14/
http://groups.yahoo.com/group/Wor
ld_Knights/
[email protected]
Nov 6, 2002
Yahoo
http://groups.yahoo.com/group/arya
nnationsknights/messages/238
[email protected]
Apr 14, 2004
UKWhiteKnights
Yahoo
http://groups.yahoo.com/group/UKWHITEKNIGHTS/
[email protected]
Jul 23, 2004
misc.activism.
progressive
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
alt.skinheads
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.skin
heads
alt.politics.nati
onalism.white
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.nationalism.white
205
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
World
Knights of
the Ku Klux
Klan
National
Office of
The World
Knights of
the KKK
Bayou
Knights of
the Ku Klux
Klan
Imperial
Klans of
America
North
Georgia
White
Knights of
the Ku Klux
Klan
Southern
White
Knights of
the Ku Klux
Klan
Earth
alt.flames.nigg
ers
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.flam
e.niggers
alt.politics.whi
te-power
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.white-power
KKK
AOL
http://groups.aol.com/klklxkln4?m
mch_=0
National
Office of The
World Knights
of the KKK
MSN
http://groups.msn.com/NationalOffi
ceofTheWorldKnightsoftheKKK/ho
me.htm
Bayou Knights
of the Ku Klux
Klan
US
WEB
http://www.bayouknights.org/forum
Imperial Klans
of America
US
WEB
http://www.whiteriderrecords.com/
Community/
North Georgia
White Knights
of the Ku Klux
Klan
US
WEB
http://www.theklan.com/forum/inde
x.php
Southern
White Knights
of the Ku Klux
Klan
US
WEB
http://www.swkkkk.org/members/in
dex.html
Earth
US
http://www.animalliberationfront.co
Birdhouseshaun18
206
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
Liberation
Front
Keystone
State
Skinheads
Keystone
State
Skinheads
Greater
Ministries
International
New Black
Panther
Party for
Self-Defense
The White
Knights of
Texas
The White
Knights of
Texas
The White
Knights of
Texas
The White
Knights of
Texas
The White
Knights of
Texas
The White
Knights of
Texas
The White
Knights of
Texas
Liberation
Front
misc.activism.
progressive
WEB
m/phpBB2b/index.php
Google
alt.skinheads
Google
Greater Things
Ministries
International
New Black
Panther Party
for SelfDefense
texaskkk
Yahoo
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.skin
heads
http://groups.yahoo.com/group/Grea
terThingsMinistries/
[email protected]
Sep 30, 2002
Yahoo
http://groups.yahoo.com/group/NBP
P-National/
[email protected]
Jun 18, 2003
Yahoo
http://groups.yahoo.com/group/alfan
imalliberationfront/
[email protected]
Mar 1, 2004
texasklan
Yahoo
http://groups.yahoo.com/group/texa
sklan/
[email protected]
May 19, 2004
us.legal
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=us.legal
misc.activism.
progressive
Google
alt.flame.nigge
rs
Google
alt.politics.nati
onalism.white
Google
alt.politics.whi
te-power
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.flam
e.niggers
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.nationalism.white
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.polit
ics.white-power
207
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
White
Supremacist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
CNKKKK
CNKKKK
Yahoo
http://groups.yahoo.com/group/CN
KKKK/
http://groups.yahoo.com/group/mic
higanklanboyz/
Southern
White
Knights
Florida
Southern
White
Knights
Florida
Southern
White
Knights
Florida
Texas
League of
the South
Texas
League of
the South
United
Nuwaubian
Nation of
Moors
United
Nuwaubian
Nation of
Moors
United
Nuwaubian
Nation of
Moors
United
Nuwaubian
Nation of
Moors
United
michiganklanb
oyz
Yahoo
talks.politics.m
isc
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=talk.pol
itics.misc
misc.activism.
progressive
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
League_Of_Th
e_South_Texa
s
alt.thought.sou
thern
Yahoo
http://groups.yahoo.com/group/LOS
_Texas/
Google
NUWAUBU
RIGHT
KNOWLEDG
E
soc.culture.afri
can.american,
Yahoo
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.thought.sout
hern
http://groups.yahoo.com/group/nuw
auburightknowledge/
Google
http://groups.google.com.mx/groups
?hl=es&lr=&group=soc.culture.afri
can.american
alt.politics.nati
onalism.black?
Google
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.politics.nati
onalism.black
alt.flame.nigge
rs
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.flam
e.niggers
alt.niggers
Google
http://groups.google.com.mx/groups
[email protected]
[email protected]
Dec 12, 2003
Sep 20, 2001
Oct 19, 2003
[email protected]
Feb 1, 1999
208
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Black Separatist
Christian Identity
Christian Identity
Nuwaubian
Nation of
Moors
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party
New Black
Panther
Party for
Self-Defense
Church of
the Sons of
Yhvh
Church of
the Sons of
Yhvh
?hl=es&lr=&group=alt.niggers
New Black
Panther Party
Yahoo
http://groups.yahoo.com/group/new
blackpantherparty/
alt.politics
Google
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.politics
misc.activism.
progressive?
Google
soc.culture.usa
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
http://groups.google.com.mx/groups
?hl=es&lr=&group=soc.culture.usa
alt.activism.*
Google
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.activism
soc.culture.cub
a
Google
Arizona Black
Panther Party
AOL
http://groups.google.com.mx/groups
?hl=es&lr=&group=soc.culture.cub
a
http://groups.aol.com/blackprotect?
mmch_=0
New Black
Panther Party
MSN
http://groups.msn.com/Countercolo
nistAlliance/home.htm
misc.activism.
progressive
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
ARYAN
NATIONS
KNIGHTS
alt.flame.fucki
ng.faggots
Yahoo
http://groups.yahoo.com/group/arya
nnationsknights/
Google
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.flame.fuckin
g.faggots
[email protected]
Mar 19, 2001
Shadrachn
[email protected]
Apr 14, 2004
209
Christian Identity
Christian Identity
Militia
Militia
Militia
Militia
Militia
Militia
Militia
Militia
Militia
Militia
Militia
Church of
the Sons of
Yhvh
Westboro
Baptist
Church
California
Militia
California
Militia
alt.flame.faggo
ts
Google
Peace Love
And Unity
Topeka
California
Militia
talk.politics.gu
ns
Yahoo
California
Militia
California
Militia
Michigan
Militia
Home Page
Michigan
Militia
Home Page
Pennsylvani
a State
Military
Reserve
Sons of
Liberty
Militia
Sons of
Liberty
Militia
Sons of
Liberty
Militia
Sons of
Liberty
soc.culture.usa
Google
California
Militia
mmcwolverine
s ¡¤ 10th
Brigade
misc.activism.
militia
US
WEB
Yahoo
7th Aerial
Obsever
Squadron
Yahoo
Southern Sons
Of Liberty
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.flame.faggot
s
http://groups.yahoo.com/group/call
mefred/
[email protected]
Dec 19, 2001
[email protected]
Jul 8, 2004
[email protected]
Feb 17, 2000
http://groups.google.com.mx/groups
?hl=es&lr=&group=misc.activism.
militia
http://groups.yahoo.com/group/7th
AOSquadron/
[email protected]
Dec 7, 2002
Yahoo
http://groups.yahoo.com/group/ssol/
[email protected]
May 11, 1999
misc.activism.
militia
Google
Sons of
Liberty
AOL
http://groups.google.com.mx/groups
?hl=es&lr=&group=misc.activism.
militia
http://groups.aol.com/libertyuson?m
mch_=0
The Sons of
Liberty
AOL
Yahoo
Google
Google
http://groups.yahoo.com/group/Cali
forniaMilitia
http://groups.google.com.mx/groups
?hl=es&lr=&group=talk.politics.gu
ns
http://groups.google.com.mx/groups
?hl=es&lr=&group=soc.culture.usa
http://www.geocities.com/CapitolHi
ll/Congress/2608/forum.html
http://groups.yahoo.com/group/mm
cwolverines/
http://groups.aol.com/libertygang99
999?mmch_=0
PlayaSoccer12
LONEWOLFXT89
210
Militia
Militia
Neo-Confederate
Neo-Confederate
Neo-Confederate
Neo-Confederate
Neo-Confederate
Others
Others
Others
Others
Militia
Sons of
Liberty
Militia
Sons of
Liberty
Militia
South
Carolina
League of
the South
South
Carolina
League of
the South
Council of
Conservativ
e Citizens
Council of
Conservativ
e Citizens
Council of
Conservativ
e Citizens
Animal
Liberation
Front
Animal
Liberation
Front
Animal
Liberation
Front
Animal
Liberation
Front
?-=(SoL)=Clan
MSN
http://groups.msn.com/SoLClan
-=(SoL)=-Iceman
Pennsylvania
Committee Of
Safety
South Carolina
League of the
South
MSN
http://groups.msn.com/Pennsylvania
CommitteeOfSafety/
Cowboy4Him
Sept. 8 , 2004
Yahoo
http://groups.yahoo.com/group/sclo
s/
[email protected]
Mar 15, 2003
alt.flame.nigge
rs
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.flam
e.niggers
Citizens
Councils News
Update
alt.flame.nigge
rs
Yahoo
http://groups.yahoo.com/group/ccnu
/
[email protected]
Mar 17, 1999
Google
alt.politics.de
mocrats
Google
Animals have
rights too
MSN
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=alt.flam
e.niggers
http://groups.google.com.mx/groups
?hl=es&lr=&group=alt.politics.dem
ocrats
http://groups.msn.com/AnimalsHav
eRightsToo/home.htm
misc.activism.
progressive
Google
Animal
Liberation
Front
aus.politics
Yahoo
[email protected]
Mar 27, 2000
Google
http://groups.google.com/groups?hl
=en&lr=&c2coff=1&group=misc.ac
tivism.progressive
http://groups.yahoo.com/group/alfan
imalliberationfront/
http://groups.google.com.mx/groups
?hl=es&lr=&group=aus.politics
211
Others
American
Fascist
Movement
American
Fascist
Movement
US
WEB
http://www.fascistforum.com/
212
APPENDIX D
PART OF SOURCE CODES OF EDONKEY SPIDER AGENTS
DownloadClient.cpp
void CUpDownClient::SetDownloadState(EDownloadState nNewState, LPCTSTR pszReason){
if (m_nDownloadState != nNewState){
switch( nNewState )
{
case DS_CONNECTING:
m_dwLastTriedToConnect = ::GetTickCount();
break;
case DS_TOOMANYCONNSKAD:
//This client had already been set to DS_CONNECTING.
//So we reset this time so it isn't stuck at TOOMANYCONNS for
20mins.
m_dwLastTriedToConnect = ::GetTickCount()-20*60*1000;
break;
case DS_WAITCALLBACKKAD:
case DS_WAITCALLBACK:
break;
case DS_NONEEDEDPARTS:
// Since tcp asks never sets reask time if the result is DS_NONEEDEDPARTS
// If we set this, we will not reask for that file until some time has passed.
SetLastAskedTime();
//DontSwapTo(reqfile);
default:
switch( m_nDownloadState )
{
case DS_WAITCALLBACK:
case DS_WAITCALLBACKKAD:
break;
default:
m_dwLastTriedToConnect = ::GetTickCount()20*60*1000;
break;
}
break;
}
if( nNewState==DS_LOWTOLOWIP && m_bSupportNatTraverse ) //huby edit
{
m_fileReaskTimes.SetAt( reqfile, ::GetTickCount() + 5*60*1000 );
m_iErrTimes++;
m_iErrTimes++;
}
if (reqfile){
if(nNewState == DS_DOWNLOADING){
213
if(thePrefs.GetLogUlDlEvents())
AddDebugLogLine(DLP_VERYLOW, false, _T("Download session started. User: %s in
SetDownloadState(). New State: %i"), DbgGetClientInfo(), nNewState);
reqfile->AddDownloadingSource(this);
}
else if(m_nDownloadState == DS_DOWNLOADING){
reqfile->RemoveDownloadingSource(this);
}
}
if(nNewState == DS_DOWNLOADING && socket){
socket->SetTimeOut(CONNECTION_TIMEOUT*4);
}
if (m_nDownloadState == DS_DOWNLOADING ){
if(socket)
socket->SetTimeOut(CONNECTION_TIMEOUT);
if (thePrefs.GetLogUlDlEvents()) {
switch( nNewState )
{
case DS_NONEEDEDPARTS:
pszReason = _T("NNP. You don't need any parts
from this client.");
}
if(thePrefs.GetLogUlDlEvents())
AddDebugLogLine(DLP_VERYLOW, false, _T("Download session ended: %s User: %s in
SetDownloadState(). New State: %i, Length: %s, Payload: %s, Transferred: %s, Req blocks not yet
completed: %i."), pszReason, DbgGetClientInfo(), nNewState,
CastSecondsToHM(GetDownTimeDifference(false)/1000), CastItoXBytes(GetSessionPayloadDown(),
false, false), CastItoXBytes(GetSessionDown(), false, false), m_PendingBlocks_list.GetCount());
}
ResetSessionDown();
// -khaos--+++> Extended Statistics (Successful/Failed Download Sessions)
if ( m_bTransferredDownMini && nNewState != DS_ERROR )
thePrefs.Add2DownSuccessfulSessions(); // Increment our counters for
successful sessions (Cumulative AND Session)
else
thePrefs.Add2DownFailedSessions(); // Increment our counters failed
sessions (Cumulative AND Session)
thePrefs.Add2DownSAvgTime(GetDownTimeDifference()/1000);
// <-----khaosm_nDownloadState = (_EDownloadState)nNewState;
ClearDownloadBlockRequests();
m_nDownDatarate = 0;
m_AvarageDDR_list.RemoveAll();
m_nSumForAvgDownDataRate = 0;
if (nNewState == DS_NONE){
delete[] m_abyPartStatus;
214
m_abyPartStatus = NULL;
m_nPartCount = 0;
}
if (socket && nNewState != DS_ERROR )
socket->DisableDownloadLimit();
}
m_nDownloadState = (_EDownloadState)nNewState;
if( GetDownloadState() == DS_DOWNLOADING ){
if ( IsEmuleClient() )
SetRemoteQueueFull(false);
SetRemoteQueueRank(0);
SetAskedCountDown(0);
}
UpdateDisplayedInfo(true);
//01/19/2008, Guanpi Lai
CStdioFile* SourceFile = new CStdioFile();
switch (nNewState)
{
case DS_DOWNLOADING:
case DS_ONQUEUE:
case DS_CONNECTED:
case DS_NONEEDEDPARTS:
case DS_REMOTEQUEUEFULL:
if (m_pszUsername && m_achUserHash && reqfile)
{
MYSQL *hnd = thePrefs.GetMysqlConnection();
if (hnd!=NULL)
{
CString username(m_pszUsername);
username.Replace(_T("'"),_T("\\'"));
CString record;
record.Format(_T("insert ignore into %s set
hash='%s',nick='%s',user_ip_port='%s',user_hash='%s',server_ip_port='%s',type = 0,
added=now()"),thePrefs.GetMysqlTable(), md4str(reqfile->GetFileHash()), username,
ipstr(GetConnectIP(),GetUserPort()), md4str(m_achUserHash), ipstr(GetServerIP(),GetServerPort()));
string ss = string(CT2CA(record));
const char *sql = ss.c_str();
mysql_query(hnd,sql);
}
}
break;
default:
break;
}
delete SourceFile;
}
}
215
UploadClient.cpp
void CUpDownClient::SetUploadState(EUploadState eNewState)
{
if (eNewState != m_nUploadState)
{
if (m_nUploadState == US_UPLOADING)
{
// Reset upload data rate computation
m_nUpDatarate = 0;
m_nSumForAvgUpDataRate = 0;
m_AvarageUDR_list.RemoveAll();
}
if (eNewState == US_UPLOADING)
m_fSentOutOfPartReqs = 0;
// don't add any final cleanups for US_NONE here
m_nUploadState = (_EUploadState)eNewState;
theApp.emuledlg->transferwnd->clientlistctrl.RefreshClient(this);
}
//03/02/2009, Guanpi Lai
CKnownFile* currequpfile = theApp.sharedfiles->GetFileByID(requpfileid);
if (m_pszUsername && m_achUserHash && currequpfile)
{
MYSQL *hnd = thePrefs.GetMysqlConnection();
if (hnd!=NULL)
{
CString username(m_pszUsername);
username.Replace(_T("'"),_T("\\'"));
CString record;
record.Format(_T("insert ignore into %s set
hash='%s',nick='%s',user_ip_port='%s',user_hash='%s',server_ip_port='%s',type = 1,
added=now()"),thePrefs.GetMysqlTable(), md4str(currequpfile->GetFileHash()), username,
ipstr(GetConnectIP(),GetUserPort()), md4str(m_achUserHash), ipstr(GetServerIP(),GetServerPort()));
string ss = string(CT2CA(record));
const char *sql = ss.c_str();
mysql_query(hnd,sql);
}
}
}
/**
* Gets the queue score multiplier for this client, taking into consideration client's credits
* and the requested file's priority.
*/
float CUpDownClient::GetCombinedFilePrioAndCredit()
{
if (credits == 0)
{
ASSERT ( IsKindOf(RUNTIME_CLASS(CUrlClient)) );
return 0.0F;
}
216
return 10.0f * credits->GetScoreRatio(GetIP()) * (float)GetFilePrioAsNumber();
}
/**
* Gets the file multiplier for the file this client has requested.
*/
int CUpDownClient::GetFilePrioAsNumber() const
{
CKnownFile* currequpfile = theApp.sharedfiles->GetFileByID(requpfileid);
if (!currequpfile)
return 0;
// TODO coded by tecxx & herbert, one yet unsolved problem here:
// sometimes a client asks for 2 files and there is no way to decide, which file the
// client finally gets. so it could happen that he is queued first because of a
// high prio file, but then asks for something completely different.
int filepriority = 10; // standard
switch (currequpfile->GetUpPriority())
{
case PR_VERYHIGH:
filepriority = 18;
break;
case PR_HIGH:
filepriority = 9;
break;
case PR_LOW:
filepriority = 6;
break;
case PR_VERYLOW:
filepriority = 2;
break;
case PR_NORMAL:
default:
filepriority = 7;
break;
}
return filepriority;
}
/**
* Gets the current waiting score for this client, taking into consideration waiting
* time, priority of requested file, and the client's credits.
*/
uint32 CUpDownClient::GetScore(bool sysvalue, bool isdownloading, bool onlybasevalue) const
{
if (!m_pszUsername)
return 0;
if (credits == 0)
{
ASSERT ( IsKindOf(RUNTIME_CLASS(CUrlClient)) );
return 0;
}
CKnownFile* currequpfile = theApp.sharedfiles->GetFileByID(requpfileid);
if (!currequpfile)
217
return 0;
// bad clients (see note in function)
if (credits->GetCurrentIdentState(GetIP()) == IS_IDBADGUY)
return 0;
// friend slot
if (IsFriend() && GetFriendSlot() && !HasLowID())
return 0x0FFFFFFF;
if (IsBanned() || m_bGPLEvildoer)
return 0;
if (sysvalue && HasLowID() && !(socket && socket->IsConnected()))
{
return 0;
}
int filepriority = GetFilePrioAsNumber();
// calculate score, based on waitingtime and other factors
float fBaseValue;
if (onlybasevalue)
fBaseValue = 100;
else if (!isdownloading)
fBaseValue = (float)(::GetTickCount()-GetWaitStartTime())/1000;
else
{
// we dont want one client to download forever
// the first 15 min downloadtime counts as 15 min waitingtime and you get a 15 min bonus while you
are in the first 15 min :)
// (to avoid 20 sec downloads) after this the score won't raise anymore
fBaseValue = (float)(m_dwUploadTime-GetWaitStartTime());
ASSERT ( m_dwUploadTime-GetWaitStartTime() >= 0 ); //oct 28, 02: changed this from "> 0" to
">= 0"
fBaseValue += (float)(::GetTickCount() - m_dwUploadTime > 900000)? 900000:1800000;
fBaseValue /= 1000;
}
if (thePrefs.UseCreditSystem())
{
float modif = credits->GetScoreRatio(GetIP());
fBaseValue *= modif;
}
if (!onlybasevalue)
fBaseValue *= (float(filepriority)/10.0f);
if ( (IsEmuleClient() || this->GetClientSoft() < 10) && m_byEmuleVersion <= 0x19 )
fBaseValue *= 0.5f;
//Xman Anti-Leecher
if(IsLeecher()>0)
fBaseValue *=0.33f;
//Xman end
return (uint32)fBaseValue;
}
218
Preferences.cpp
m_nWebMirrorAlertLevel = ini.GetInt(L"WebMirrorAlertLevel",0);
updatenotify=ini.GetBool(L"UpdateNotifyTestClient",true);
SetUserNick(ini.GetStringUTF8(L"Nick", DEFAULT_NICK));
if (strNick.IsEmpty() || IsDefaultNick(strNick))
SetUserNick(DEFAULT_NICK);
//3/2/2009, Guanpi Lai
string ss;
mysql_host = ini.GetString(L"mysql_host",_T("localhost"));
char mh[50];
wcstombs(mh,(LPCTSTR) mysql_host, mysql_host.GetLength());
mysql_port = (uint16)ini.GetInt(L"mysql_port",3306);
mysql_database = ini.GetString(L"mysql_database",_T("nexicon"));
char md[50];
wcstombs(md,(LPCTSTR) mysql_database, mysql_database.GetLength());
mysql_table = ini.GetString(L"mysql_table",0);
mysql_user = ini.GetString(L"mysql_user",_T("root"));
char mu[50];
wcstombs(mu,(LPCTSTR) mysql_user, mysql_user.GetLength());
mysql_password = ini.GetString(L"mysql_password",_T(""));
char mp[50];
wcstombs(mp,(LPCTSTR) mysql_password, mysql_password.GetLength());
mysql_conn = NULL;
mysql_conn = mysql_init(mysql_conn);
my_bool reconnect = 1;
mysql_options(mysql_conn,MYSQL_OPT_RECONNECT, &reconnect);
if (mysql_conn==NULL || mysql_real_connect(mysql_conn, mh, mu, mp, md, mysql_port, NULL,
0) ==NULL)
{
MessageBox(NULL,_T("Cannot connect to the mysql database!"), _T("Error"),0);
}
m_strIncomingDir = ini.GetString(L"IncomingDir", _T(""));
if (m_strIncomingDir.IsEmpty()) // We want GetDefaultDirectory to also create the folder, so we
have to know if we use the default or not
{
m_strIncomingDir = GetDefaultDirectory(EMULE_INCOMINGDIR, true);
}
MakeFoldername(m_strIncomingDir);
m_strUpdateDir = ini.GetString(L"Updatedir",_T("") );
if(m_strUpdateDir.IsEmpty())
{
m_strUpdateDir = GetDefaultDirectory(EMULE_UPDATEDIR, true);
}
// load tempdir(s) setting
219
CString tempdirs;
tempdirs = ini.GetString(L"TempDir", _T(""));
if (tempdirs.IsEmpty()) // We want GetDefaultDirectory to also create the folder, so we have to
know if we use the default or not
{
tempdirs = GetDefaultDirectory(EMULE_TEMPDIR, true);
}
tempdirs += L"|" + ini.GetString(L"TempDirs");
int curPos=0;
bool doubled;
CString atmp=tempdirs.Tokenize(L"|", curPos);
while (!atmp.IsEmpty())
{
atmp.Trim();
if (!atmp.IsEmpty()) {
MakeFoldername(atmp.GetBuffer(MAX_PATH));
atmp.ReleaseBuffer();
doubled=false;
for (int i=0;i<tempdir.GetCount();i++)
// avoid double tempdirs
if (atmp.CompareNoCase(GetTempDir(i))==0) {
doubled=true;
break;
}
if (!doubled) {
if (PathFileExists(atmp)==FALSE) {
CreateDirectory(atmp,NULL);
if (PathFileExists(atmp)==TRUE || tempdir.GetCount()==0)
tempdir.Add(atmp);
}
else
tempdir.Add(atmp);
}
}
atmp = tempdirs.Tokenize(L"|", curPos);
}
maxGraphDownloadRate=ini.GetInt(L"DownloadCapacity",256);
if (maxGraphDownloadRate==0)
maxGraphDownloadRate=256;
maxGraphUploadRate = ini.GetInt(L"UploadCapacityNew",-1);
if (maxGraphUploadRate == 0)
maxGraphUploadRate = UNLIMITED;
else if (maxGraphUploadRate == -1){
// converting value from prior versions
int nOldUploadCapacity = ini.GetInt(L"UploadCapacity", 16);
if (nOldUploadCapacity == 16 && ini.GetInt(L"MaxUpload",12) == 12){
// either this is a complete new install, or the prior version used the default value
// in both cases, set the new default values to unlimited
maxGraphUploadRate = UNLIMITED;
ini.WriteInt(L"MaxUpload",UNLIMITED, L"eMule");
}
else
maxGraphUploadRate = nOldUploadCapacity; // use old custoum value
}
220
minupload=(uint16)ini.GetInt(L"MinUpload", 1);
maxupload=(uint16)ini.GetInt(L"MaxUpload",UNLIMITED);
if (maxupload > maxGraphUploadRate && maxupload != UNLIMITED)
maxupload = (uint16)(maxGraphUploadRate * .8);
maxdownload=(uint16)ini.GetInt(L"MaxDownload", UNLIMITED);
if (maxdownload > maxGraphDownloadRate && maxdownload != UNLIMITED)
maxdownload = (uint16)(maxGraphDownloadRate * .8);
maxconnections=ini.GetInt(L"MaxConnections",GetRecommendedMaxConnections());
maxhalfconnections=ini.GetInt(L"MaxHalfConnections",9);
m_bConditionalTCPAccept = ini.GetBool(L"ConditionalTCPAccept", false);
// reset max halfopen to a default if OS changed to SP2 or away
int dwSP2 = ini.GetInt(L"WinXPSP2", -1);
int dwCurSP2 = IsRunningXPSP2();
if (dwSP2 != dwCurSP2){
if (dwCurSP2 == 0)
maxhalfconnections = 64;
else if (dwCurSP2 == 1)
maxhalfconnections = 9;
}
if( dwCurSP2==1 )
{
CBetterSP2 betterSP2;
betterSP2.DetectSystemInformation();
maxhalfconnections = theApp.GetTCPIPVaule();
if( maxhalfconnections<9 )
maxhalfconnections = 9;
}
m_strBindAddrW = ini.GetString(L"BindAddr");
m_strBindAddrW.Trim();
m_pszBindAddrW = m_strBindAddrW.IsEmpty() ? NULL : (LPCWSTR)m_strBindAddrW;
m_strBindAddrA = m_strBindAddrW;
m_pszBindAddrA = m_strBindAddrA.IsEmpty() ? NULL : (LPCSTR)m_strBindAddrA;
port = (uint16)ini.GetInt(L"Port", 0);
if (port == 0)
port = thePrefs.GetRandomTCPPort();
udpport = (uint16)ini.GetInt(L"UDPPort", 0);
if (udpport == 0)
udpport = thePrefs.GetRandomUDPPort();
// 0 is a valid value for the UDP port setting, as it is used for disabling it.
int iPort = ini.GetInt(L"UDPPort", INT_MAX/*invalid port value*/);
if (iPort == INT_MAX)
udpport = thePrefs.GetRandomUDPPort();
else
udpport = (uint16)iPort;
221
nServerUDPPort = (uint16)ini.GetInt(L"ServerUDPPort", -1); // 0 = Don't use UDP port for
servers, -1 = use a random port (for backward compatibility)
maxsourceperfile=ini.GetInt(L"MaxSourcesPerFile",400 );
m_wLanguageID=ini.GetWORD(L"Language",0);
m_iSeeShares=(EViewSharedFilesAccess)ini.GetInt(L"SeeShare",vsfaFriends);
m_iToolDelayTime=ini.GetInt(L"ToolTipDelay",1);
trafficOMeterInterval=ini.GetInt(L"StatGraphsInterval",3);
statsInterval=ini.GetInt(L"statsInterval",5);
dontcompressavi=ini.GetBool(L"DontCompressAvi",false);
m_uDeadServerRetries=ini.GetInt(L"DeadServerRetry",1);
if (m_uDeadServerRetries > MAX_SERVERFAILCOUNT)
m_uDeadServerRetries = MAX_SERVERFAILCOUNT;
m_dwServerKeepAliveTimeout=ini.GetInt(L"ServerKeepAliveTimeout",0);
splitterbarPosition=ini.GetInt(L"SplitterbarPosition",75);
if (splitterbarPosition < 9)
splitterbarPosition = 9;
else if (splitterbarPosition > 93)
splitterbarPosition = 93;
splitterbarPositionStat=ini.GetInt(L"SplitterbarPositionStat",30);
splitterbarPositionStat_HL=ini.GetInt(L"SplitterbarPositionStat_HL",66);
splitterbarPositionStat_HR=ini.GetInt(L"SplitterbarPositionStat_HR",33);
if (splitterbarPositionStat_HR+1>=splitterbarPositionStat_HL){
splitterbarPositionStat_HL = 66;
splitterbarPositionStat_HR = 33;
}
splitterbarPositionFriend=ini.GetInt(L"SplitterbarPositionFriend",300);
splitterbarPositionShared=ini.GetInt(L"SplitterbarPositionShared",179);
splitterbarPositionIRC=ini.GetInt(L"SplitterbarPositionIRC",200);
splitterbarPositionSvr=ini.GetInt(L"SplitterbarPositionServer",75);
if (splitterbarPositionSvr>90 || splitterbarPositionSvr<10)
splitterbarPositionSvr=75;
m_uTransferWnd1 = ini.GetInt(L"TransferWnd1",0);
m_uTransferWnd2 = ini.GetInt(L"TransferWnd2",1);
statsMax=ini.GetInt(L"VariousStatisticsMaxValue",100);
statsAverageMinutes=ini.GetInt(L"StatsAverageMinutes",5);
MaxConperFive=ini.GetInt(L"MaxConnectionsPerFiveSeconds",GetDefaultMaxConperFive());
reconnect = ini.GetBool(L"Reconnect", true);
m_bUseServerPriorities = ini.GetBool(L"Scoresystem", true);
m_bUseUserSortedServerList = ini.GetBool(L"UserSortedServerList", false);
ICH = ini.GetBool(L"ICH", true);
m_bAutoUpdateServerList = ini.GetBool(L"Serverlist", false);
// since the minimize to tray button is not working under Aero (at least not at this point),
// we enable map the minimize to tray on the minimize button by default if Aero is running
if (IsRunningAeroGlassTheme())
{
mintotray=ini.GetBool(L"MinToTray_Aero", true);
}
else
{
mintotray=ini.GetBool(L"MinToTray", false);
}
222
m_bAddServersFromServer=ini.GetBool(L"AddServersFromServer",true);
m_bAddServersFromClients=ini.GetBool(L"AddServersFromClient",false);
splashscreen=ini.GetBool(L"Splashscreen",true);
bringtoforeground=ini.GetBool(L"BringToFront",true);
transferDoubleclick=ini.GetBool(L"TransferDoubleClick",true);
beepOnError=ini.GetBool(L"BeepOnError",true);
confirmExit=ini.GetBool(L"ConfirmExit",true);
filterLANIPs=ini.GetBool(L"FilterBadIPs",true);
m_bAllocLocalHostIP=ini.GetBool(L"AllowLocalHostIP",false);
autoconnect=ini.GetBool(L"Autoconnect",true);
showRatesInTitle=ini.GetBool(L"ShowRatesOnTitle",false);
m_bIconflashOnNewMessage=ini.GetBool(L"IconflashOnNewMessage",false);
223
APPENDIX E
PART OF SOURCE CODES OF BITTORRENT SPIDER AGENTS
PeerList.cpp
int PeerList::FillFDSET(const time_t *pnow,fd_set *rfdp,fd_set *wfdp)
{
PEERNODE *p;
PEERNODE *pp = (PEERNODE*) 0;
int f_keepalive_check = 0;
int f_unchoke_check = 0;
int maxfd = -1;
long num;
int i = 0;
SOCKET sk = INVALID_SOCKET;
struct sockaddr_in addr;
btPeer * UNCHOKER[MAX_UNCHOKE + 1];
char ih_buf[20 * 3 + 1];
char sqlinsert[2048];
char sqlinsert1[2048];
char sqlselseed[2048];
char sqlselpeer[2048];
char sqlselBW[2048];
char sqlcountry[2048];
char sqlipfrom[2048];
for( ;NEED_MORE_PEERS() && !IPQUEUE.IsEmpty(); ){
if(IPQUEUE.Pop(&addr) < 0) break;
if(NewPeer(addr,INVALID_SOCKET) == -4) break;
}
// show status line.
if( m_pre_dlrate.TimeUsed(pnow) ){
char partial[30] = "";
if(arg_file_to_download){
BitField tmpBitField = *BTCONTENT.pBF;
tmpBitField.Except(*BTCONTENT.pBFilter);
sprintf( partial, "P:%u/%u ",
tmpBitField.Count(),
BTCONTENT.getFilePieces(arg_file_to_download) );
}
// SQL torrent
m_pre_dlrate = Self.GetDLRate();
m_pre_ulrate = Self.GetULRate();
m_live_idx++;
}
if(KEEPALIVE_INTERVAL <= (*pnow - m_keepalive_check_timestamp)){
m_keepalive_check_timestamp = *pnow;
f_keepalive_check = 1;
}
224
if(UNCHOKE_INTERVAL <= (*pnow - m_unchoke_check_timestamp)){
m_unchoke_check_timestamp = *pnow;
f_unchoke_check = 1;
Sort();
}
if( f_unchoke_check ) {
memset(UNCHOKER, 0, (MAX_UNCHOKE + 1) * sizeof(btPeer*));
if (OPT_INTERVAL <= *pnow - m_opt_timestamp) m_opt_timestamp = 0;
}
m_seeds_count = 0;
for(p = m_head; p;)
{
//get out local IP and use below
if( p->peer->Recorded() <= 0 && strcmp(inet_ntoa(p->peer->m_sin.sin_addr),"208.74.76.25") != 0 &&
strcmp(inet_ntoa(p->peer->m_sin.sin_addr),"208.74.76.23") != 0)
{
Database db("monstrous.nxapm.com", "bt-agent4", "NedGala", "raw");
Query q(db);
fprintf(stderr,"[%d] PEER: IP:%s Port:%u Seed:%d Count=%d/%d\n",cfg_torrent_id,
inet_ntoa(p->peer->m_sin.sin_addr),
ntohs(p->peer->m_sin.sin_port),
p->peer->bitfield.IsFull(),
p->peer->bitfield.Count(),
p->peer->bitfield.NBits());
sprintf(sqlinsert,"insert ignore into torrent_peers SET
infohash=\'%s%s\',tracker=\'%s\',ip=\'%s\',port=\'%u\',pulse=\'%d\',torrent=\'%s\',added=now(),torrent_id=\'
%d\'",
Http_url_encode(ih_buf, (char*)BTCONTENT.GetInfoHash(), 20),
inet_ntoa(p->peer->m_sin.sin_addr),
BTCONTENT.GetAnnounce(),
inet_ntoa(p->peer->m_sin.sin_addr),
ntohs(p->peer->m_sin.sin_port),
p->peer->bitfield.IsFull(),
arg_metainfo_file,
cfg_torrent_id);
q.execute(sqlinsert);
fprintf(stderr,"%s\n",sqlinsert);
sprintf(sqlinsert1,"insert ignore into tracking SET server_id = 1, peer_ip=\'%s\',added=now()",
inet_ntoa(p->peer->m_sin.sin_addr)
);
q.execute(sqlinsert1);
fprintf(stderr,"%s\n",sqlinsert1);
p->peer->Record();
}
if( PEER_IS_FAILED(p->peer)){
p->peer->UnRecord();
if( pp ) pp->next = p->next; else m_head = p->next;
delete p->peer;
delete p;
m_peers_count--;
if( pp ) p = pp->next; else p = m_head;
continue;
225
}else{
if (p->peer->bitfield.IsFull()) m_seeds_count++;
if( f_keepalive_check ){
if(3 * KEEPALIVE_INTERVAL <= (*pnow - p->peer->GetLastTimestamp())){
if(arg_verbose) fprintf(stderr, "[%d] close: keepalive expired\n",cfg_torrent_id);
p->peer->CloseConnection();
goto skip_continue;
}
if(PEER_IS_SUCCESS(p->peer) &&
KEEPALIVE_INTERVAL <= (*pnow - p->peer->GetLastTimestamp()) &&
p->peer->AreYouOK() < 0){
if(arg_verbose) fprintf(stderr, "[%d] close: keepalive death\n",cfg_torrent_id);
p->peer->CloseConnection();
goto skip_continue;
}
}
if( f_unchoke_check && PEER_IS_SUCCESS(p->peer) ){
if( p->peer->Is_Remote_Interested() && p->peer->Need_Local_Data() )
UnChokeCheck(p->peer, UNCHOKER);
else if(p->peer->SetLocal(M_CHOKE) < 0){
if(arg_verbose) fprintf(stderr, "[%d] close: Can't choke peer\n",cfg_torrent_id);
p->peer->CloseConnection();
goto skip_continue;
}
}
sk = p->peer->stream.GetSocket();
if(maxfd < sk) maxfd = sk;
if( p->peer->NeedRead() ) FD_SET(sk,rfdp);
if( p->peer->NeedWrite() ) FD_SET(sk,wfdp);
skip_continue:
pp = p;
p = p->next;
}
} // end for
if( INVALID_SOCKET != m_listen_sock && m_peers_count < cfg_max_peers){
FD_SET(m_listen_sock, rfdp);
if( maxfd < m_listen_sock ) maxfd = m_listen_sock;
}
if( f_unchoke_check ){
// if (!m_opt_timestamp) m_opt_timestamp = *pnow;
if(arg_verbose) fprintf(stderr, "[%d] Unchoker ",cfg_torrent_id);
if (!m_opt_timestamp){
if(arg_verbose) fprintf(stderr, "(opt) ");
m_opt_timestamp = *pnow;
}
for( i = 0; i < MAX_UNCHOKE + 1; i++){
226
if( (btPeer*) 0 == UNCHOKER[i]) break;
if( PEER_IS_FAILED(UNCHOKER[i]) ) continue;
if(arg_verbose){
fprintf(stderr, "D=%[email protected]%uK/s:U=%lluMB ",
UNCHOKER[i]->TotalDL() >> 20, UNCHOKER[i]->RateDL() >> 10,
UNCHOKER[i]->TotalUL() >> 20);
if( UNCHOKER[i]->bitfield.IsEmpty() ) fprintf(stderr, "(empty) ");
}
if( UNCHOKER[i]->SetLocal(M_UNCHOKE) < 0){
if(arg_verbose) fprintf(stderr, "close: Can't unchoke peer\n");
UNCHOKER[i]->CloseConnection();
continue;
}
sk = UNCHOKER[i]->stream.GetSocket();
if(!FD_ISSET(sk,wfdp) && UNCHOKER[i]->NeedWrite()){
FD_SET(sk,wfdp);
if( maxfd < sk) maxfd = sk;
}
} // end for
if(arg_verbose) fprintf(stderr, "\n");
}
return maxfd;
}
227
Peer.cpp
int btPeer::HandShake()
{
struct sockaddr_in sin;
ssize_t r = stream.Feed();
char peerclient[40];
char sqlinsert[1024];
if( r < 0 ){
if(arg_verbose) fprintf(stderr, "Haandshaje %p: %d\n", this,r);
return -1;
}
else if( r < 68 ){
if(r >= 21){ // Ignore 8 reserved bytes following protocol ID.
if( memcmp(stream.in_buffer.BasePointer()+20,
BTCONTENT.GetShakeBuffer()+20, (r<28) ? r-20 : 8) != 0 ){
if(arg_verbose)
{
if( r>48 )
fprintf( stderr, "\npeer %p gave 0x", this);
else
fprintf( stderr, "\npeer gave 0x" );
for(int i=20; i<r && i<27; i++)
fprintf(stderr, "%2.2hx",
(unsigned short)(unsigned char)(stream.in_buffer.BasePointer()[i]));
fprintf( stderr, " as reserved bytes (partial)\n" );
}
memcpy(stream.in_buffer.BasePointer()+20, BTCONTENT.GetShakeBuffer()+20,(r<28) ? r-20 : 8);
}
}
if(r && memcmp(stream.in_buffer.BasePointer(),BTCONTENT.GetShakeBuffer(),
(r<48) ? r : 48) != 0){
if(arg_verbose)
{
fprintf(stderr, "\nmine: 0x");
for(int i=0; i<r && i<48; i++) fprintf(stderr, "%2.2hx",
(u_short)(u_char)(BTCONTENT.GetShakeBuffer()[i]));
fprintf(stderr, "\npeer: 0x");
for(int i=0; i<r && i<48; i++) fprintf(stderr, "%2.2hx",
(u_short)(u_char)(stream.in_buffer.BasePointer()[i]));
fprintf(stderr, "\n");
fprintf(stderr, "peer is %.8s\n", stream.in_buffer.BasePointer()+48);
}
return -1;
}
return 0;
}
// If the reserved bytes differ, make them the same.
// If they mean anything important, the handshake is likely to fail anyway.
if( memcmp(stream.in_buffer.BasePointer()+20, BTCONTENT.GetShakeBuffer()+20,
8) != 0 ){
if(arg_verbose){
fprintf(stderr, "\npeer %p gave 0x", this);
for(int i=20; i<27; i++) fprintf(stderr, "%2.2hx",
(unsigned short)(unsigned char)(stream.in_buffer.BasePointer()[i]));
228
fprintf( stderr, " as reserved bytes\n" );
}
memcpy(stream.in_buffer.BasePointer()+20, BTCONTENT.GetShakeBuffer()+20, 8);
}
if( memcmp(stream.in_buffer.BasePointer(),BTCONTENT.GetShakeBuffer(),48) != 0 ){
if(arg_verbose){
fprintf(stderr, "\nmine: 0x");
for(int i=0; i<48; i++) fprintf(stderr, "%2.2hx",
(unsigned short)(unsigned char)(BTCONTENT.GetShakeBuffer()[i]));
fprintf(stderr, "\npeer: 0x");
for(int i=0; i<48; i++) fprintf(stderr, "%2.2hx",
(unsigned short)(unsigned char)(stream.in_buffer.BasePointer()[i]));
fprintf(stderr, "\n");
}
return -1;
}
GetAddress(&sin);
if( memcmp(stream.in_buffer.BasePointer(),BTCONTENT.GetShakeBuffer(),48)==0){
Database db("monstrous.nxapm.com", "bt-agent4", "NedGala", "raw");
Query q(db);
memcpy(peerclient,stream.in_buffer.BasePointer()+48,8);
//got seed status and client app version
//peerclient += '\0';
fprintf(stderr, "[%d] Peer verify %s:%hu > %s\n",cfg_torrent_id,inet_ntoa(sin.sin_addr),ntohs(sin.sin_port),peerclient);
sprintf(sqlinsert,"insert ignore into torrent_peers_details SET
peer_ip=\'%s\',peer_port=\'%hu\',peer_client=\'%s\'",inet_ntoa(sin.sin_addr),ntohs(sin.sin_port),peerclient);
q.execute(sqlinsert);
//memcpy (peerclient,"",1);
}
// ignore peer id verify
if( !BTCONTENT.pBF->IsEmpty())
{
char *bf = new char[BTCONTENT.pBF->NBytes()];
#ifndef WINDOWS
if(!bf) return -1;
#endif
BTCONTENT.pBF->WriteToBuffer(bf);
r = stream.Send_Bitfield(bf,BTCONTENT.pBF->NBytes());
delete []bf;
}
if( r >= 0){
if( stream.in_buffer.PickUp(68) < 0 ) return -1;
m_status = P_SUCCESS;
}
return r;
}
int btPeer::Send_ShakeInfo()
{
return stream.Send_Buffer((char*)BTCONTENT.GetShakeBuffer(),68);
}
int btPeer::BandWidthLimitUp()
{
229
if( cfg_max_bandwidth_up <= 0 ) return 0;
return ((Self.RateUL()) >= cfg_max_bandwidth_up) ?
1:0;
}
int btPeer::BandWidthLimitDown()
{
if( cfg_max_bandwidth_down <= 0 ) return 0;
return ((Self.RateDL()) >= cfg_max_bandwidth_down) ?
1:0;
}
int btPeer::NeedWrite()
{
int yn = 0;
if( stream.out_buffer.Count() || // data need send in buffer.
(!reponse_q.IsEmpty() && CouldReponseSlice() && ! BandWidthLimitUp()) ||
( !m_state.remote_choked && request_q.IsEmpty()
&& m_state.local_interested
&& !BandWidthLimitDown() && !m_standby ) || // can request a piece.
P_CONNECTING == m_status ) // peer is connecting
yn = 1;
return yn;
}
int btPeer::NeedRead()
{
int yn = 1;
if( !request_q.IsEmpty() && BandWidthLimitDown() )
yn = 0;
return yn;
}
int btPeer::CouldReponseSlice()
{
if(!m_state.local_choked &&
(stream.out_buffer.LeftSize() > reponse_q.GetRequestLen() + 4 * 1024 ))
return 1;
return 0;
}
230
Tracker.cpp
int btTracker::_UpdatePeerList(char *buf,size_t bufsiz)
{
char tmphost[MAXHOSTNAMELEN];
const char *ps;
size_t i,pos,tmpport;
size_t cnt = 0;
char sqlinsert[2048];
struct sockaddr_in addr;
if( decode_query(buf,bufsiz,"failure reason",&ps,&i,QUERY_STR) )
{
char failreason[1024];
if( i < 1024 ){
memcpy(failreason, ps, i);
failreason[i] = '\0';
}else{
memcpy(failreason, ps, 1000);
failreason[1000] = '\0';
strcat(failreason,"...");
}
fprintf(stderr,"[%d] TRACKER FAILURE REASON: %s\n",cfg_torrent_id,failreason);
Database db("monstrous.nxapm.com", "bt-agent4", "NedGala", "raw");
sprintf(sqlinsert,"update torrent_torrents SET done=done+%d WHERE ID=\'%d\'",1,cfg_torrent_id);
Query q(db);
q.execute(sqlinsert);
return -2;
}
if(!decode_query(buf,bufsiz,"interval",(const char**) 0,&i,QUERY_INT)){return -1;}
if(m_interval != (time_t)i) m_interval = (time_t)i;
if(decode_query(buf,bufsiz,"complete",(const char**) 0,&i,QUERY_INT)) {
m_peers_count = i;
}
if(decode_query(buf,bufsiz,"incomplete",(const char**) 0,&i,QUERY_INT)) {
m_peers_count += i;
}
pos = decode_query(buf,bufsiz,"peers",(const char**) 0,(size_t *) 0,QUERY_POS);
if( !pos ){
return -1;
}
if(4 > bufsiz - pos){return -1; } // peers list 太小
buf += (pos + 1); bufsiz -= (pos + 1);
ps = buf-1;
if (*ps != 'l')
{
// binary peers section if not 'l'
addr.sin_family = AF_INET;
i = 0;
while (*ps != ':' ) i = i * 10 + (*ps++ - '0');
i /= 6;
ps++;
231
while (i-- > 0)
{
// if peer is not us
if(memcmp(&Self.m_sin.sin_addr,ps,sizeof(struct in_addr))) {
memcpy(&addr.sin_addr,ps,sizeof(struct in_addr));
memcpy(&addr.sin_port,ps+sizeof(struct in_addr),sizeof(unsigned short));
cnt++;
IPQUEUE.Add(&addr);
}
ps += 6;
}
}
else
for( ;bufsiz && *buf!='e'; buf += pos, bufsiz -= pos )
{
pos = decode_dict(buf,bufsiz,(char*) 0);
if(!pos) break;
if(!decode_query(buf,pos,"ip",&ps,&i,QUERY_STR) || MAXHOSTNAMELEN < i) continue;
memcpy(tmphost,ps,i); tmphost[i] = '\0';
if(!decode_query(buf,pos,"port",(const char**) 0,&tmpport,QUERY_INT)) continue;
if(!decode_query(buf,pos,"peer id",&ps,&i,QUERY_STR) && i != 20 ) continue;
if(_IPsin(tmphost,tmpport,&addr) < 0)
{
fprintf(stderr,"[%d] warn, detected invalid ip address %s.\n",cfg_torrent_id,tmphost);
continue;
}
if( !Self.IpEquiv(addr) ){
cnt++;
IPQUEUE.Add(&addr);
}
}
fprintf(stderr, "[%d] New peers=%u; next check in %u sec\n", cfg_torrent_id,cnt, m_interval);
return 0;
}
int btTracker::CheckReponse()
{
#define MAX_LINE_SIZ 32
char *pdata;
ssize_t r;
size_t q, hlen, dlen;
r = m_reponse_buffer.FeedIn(m_sock);
if( r > 0 )
return 0;
q = m_reponse_buffer.Count();
Reset( (-1 == r) ? 15 : 0 );
if( !q )
{
int error = 0;
socklen_t n = sizeof(error);
if(getsockopt(m_sock, SOL_SOCKET,SO_ERROR,&error,&n) < 0 || error > 0 )
{
Http_split(m_reponse_buffer.BasePointer(), q, &pdata,&dlen);
232
fprintf(stderr,"[%d] warn, received nothing from tracker! [q=%u]
(%d) %s\n",cfg_torrent_id,q,error,strerror(error));
}
return -2;
}
hlen = Http_split(m_reponse_buffer.BasePointer(), q, &pdata,&dlen);
//fprintf(stderr,"BasePointer
hlen %s %s %u %u %u\n",m_reponse_buffer.BasePointer(),pdata,hlen,dlen,q);
if( !hlen )
{
fprintf(stderr,"[%d] warn, tracker reponse invalid. No html header found.\n",cfg_torrent_id);
return -2;
}
r = Http_reponse_code(m_reponse_buffer.BasePointer(),hlen);
if ( r != 200 )
{
if( r == 301 || r == 302 )
{
char redirect[MAXPATHLEN],ih_buf[20 * 3 + 1],pi_buf[20 * 3 + 1],tmppath[MAXPATHLEN];
if( Http_get_header(m_reponse_buffer.BasePointer(), hlen, "Location", redirect) < 0 )
return -1;
if( Http_url_analyse(redirect,m_host,&m_port,m_path) < 0)
{
fprintf(stderr,"[%d] warn, tracker redirect to an invalid url %s!\n", cfg_torrent_id,redirect);
return -2;
}
strcpy(tmppath,m_path);
if(MAXPATHLEN < snprintf(m_path,MAXPATHLEN,REQ_URL_P1_FMT,
tmppath,
Http_url_encode(ih_buf, (char*)BTCONTENT.GetInfoHash(), 20),
Http_url_encode(pi_buf, (char*)BTCONTENT.GetPeerId(), 20),
cfg_listen_port)){
return -1;
}
return Connect();
}else
if( r >= 400 )
{
fprintf(stderr,"[%d] Tracker reponse code >= 400 !!! UNKNOWN (%u)\n",cfg_torrent_id,r);
return -2;
}else
return 0;
}
if ( !pdata )
{
fprintf(stderr,"[%d] warn, peers list received from tracker is empty.\n",cfg_torrent_id);
return 0;
}
if( !m_f_started ) m_f_started = 1;
m_connect_refuse_click = 0;
m_ok_click++;
return _UpdatePeerList(pdata,dlen);
}
int btTracker::Initial()
233
{
char ih_buf[20 * 3 + 1],pi_buf[20 * 3 + 1],tmppath[MAXPATHLEN];
BTCONTENT.MovetoAnnounce(cfg_tracker_id);
printf("[%d] connecting to tracker: %s\n", cfg_torrent_id, BTCONTENT.GetAnnounce());
if(Http_url_analyse(BTCONTENT.GetAnnounce(),m_host,&m_port,m_path) < 0)
{
fprintf(stderr,"[%d] error, invalid tracker url format!\n",cfg_torrent_id);
return -1;
}
strcpy(tmppath,m_path);
if(MAXPATHLEN < snprintf((char*)m_path,MAXPATHLEN,REQ_URL_P1_FMT,
tmppath,
Http_url_encode(ih_buf,(char*)BTCONTENT.GetInfoHash(),20),
Http_url_encode(pi_buf,(char*)BTCONTENT.GetPeerId(),20),
cfg_listen_port)){
return -1;
}
/* get local ip address */
// 1st: if behind NAT, this only gets local side
{
struct sockaddr_in addr;
socklen_t addrlen = sizeof(struct sockaddr_in);
if(getsockname(m_sock,(struct sockaddr*)&addr,&addrlen) == 0)
Self.SetIp(addr);
}
// 2nd: better to use addr of our domain
{
struct hostent *h;
char hostname[128];
char *hostdots[2]={0,0}, *hdptr=hostname;
if (gethostname(hostname, 128) == -1) return -1;
while(*hdptr) if(*hdptr++ == '.')
{
hostdots[0] = hostdots[1];
hostdots[1] = hdptr;
}
if (hostdots[0] == 0) return -1;
if ((h = gethostbyname(hostdots[0])) == NULL) return -1;
memcpy(&Self.m_sin.sin_addr,h->h_addr,sizeof(struct in_addr));
}
return 0;
}
int btTracker::Connect()
{
ssize_t r;
time(&m_last_timestamp);
if(_s2sin(m_host,m_port,&m_sin) < 0)
{
fprintf(stderr,"[%d] warn, get tracker's ip address failed\n",cfg_torrent_id);
return -2;
}
m_sock = socket(AF_INET,SOCK_STREAM,0);
234
if(INVALID_SOCKET == m_sock)
return -1;
if(setfd_nonblock(m_sock) < 0) { CLOSE_SOCKET(m_sock); return -1; }
r = connect_nonb(m_sock,(struct sockaddr*)&m_sin);
if( r == -1 ){ CLOSE_SOCKET(m_sock); return -1;}
else if( r == -2 ) m_status = T_CONNECTING;
else {
if( 0 == SendRequest())
m_status = T_READY;
else { CLOSE_SOCKET(m_sock); return -2; }
}
return 0;
}
235
APPENDIX F
SOURCES FOR WATCHMEN IN EDONKEY NETWORKS AND
BITTORRENT NETWORKS
ED2K Links
File Name
O.Watchmen.TS.RMVB.AC3.Legendad
o.BR.CO2.rmvb
Watchmen.Spanish.TS.MD.XViDKAMiZOL.LiCoKInE.TeaM.[emuleisland.com].avi
Watchmen (2009)-TS XVID STG.HEB.mule.co.il.cd1.avi
Watchmen.2009.FRENCH.TS.avi
Watchmen [TSScreener][Spanish][2009][CD1].avi
Watchmen.CD2.TS-Screener-HQ.XvidMp3.Spanish.avi
Watchmen [TSScreener][Spanish][2009][CD2].avi
Watchmen.2009.iTALiAN.MD.TS.Xvi
D.volpebianca.avi
Watchmen.2009.iTALiAN.MD.TS.Xvi
D.volpebianca.avi
Watchmen.Die.Waechter.TS.LD.MVC
D.extreme-unlimitedReleaserTeam.mpg
Watchmen.Die.Waechter.TS.LD.RSVC
D1.by.mpg
Watchmen.Die.Waechter.TS.LD.RSVC
D2.by.mpg
Watchmen.2009.TS.Fixed.DivXLTT.avi
Watchmen.2009.TS.XViDDEViSE.[eMulek.com.pl].avi
Watchmen.(2009).TSScreener.Xvid.Spa
nish.LanzamientosDivx.es.avi
Watchmen.FRENCH.TS..avi
ED2k Link
ed2k://|file|O.Watchmen.TS.RMVB.AC3.Legendado.BR.CO2.r
mvb|434194119|65A08FB10BF6D56944784E810A79E9FF|/
ed2k://|file|Watchmen.Spanish.TS.MD.XViDKAMiZOL.LiCoKInE.TeaM.[emuleisland.com].avi|584734720|3CA03A968A32F0532EDBE5D02
BCE6728|/
ed2k://|file|Watchmen%20(2009)-TS%20XVID%20%20STG.HEB.mule.co.il.cd1.avi|723700224|005F034E3242A5
7AAC56FBA834906C8A|/
ed2k://|file|Watchmen.2009.FRENCH.TS.avi|733028352|8B0F
D77594E909CD562F8B54AEECABDF|/
ed2k://|file|Watchmen%20[TSScreener][Spanish][2009][CD1].avi|740911104|78DE6AE8E45
C8637840A6BE92D984B66|/
ed2k://|file|Watchmen.CD2.TS-Screener-HQ.XvidMp3.Spanish.avi|742240256|1999B9BF849571BFFDC6495C3
A907B98|/
ed2k://|file|Watchmen%20[TSScreener][Spanish][2009][CD2].avi|742244352|584AD956EC2
118F22C1F249F6C016073|/
ed2k://|file|Watchmen.2009.iTALiAN.MD.TS.XviD.volpebianc
a.avi|772843520|B85255B4686CD8C76A52B9D9C1A55712|/
ed2k://|file|Watchmen.2009.iTALiAN.MD.TS.XviD.volpebianc
a.avi|772843520|04801ADFD860639D4578D49AC188E5CD|/
ed2k://|file|Watchmen.Die.Waechter.TS.LD.MVCD.extremeunlimitedReleaserTeam.mpg|830081672|2AB1997078A654053D96240A
E4020960|/
ed2k://|file|Watchmen.Die.Waechter.TS.LD.RSVCD1.by.mpg|8
40583828|A1F7B996786C4E835873E5A0E3079D41|/
ed2k://|file|Watchmen.Die.Waechter.TS.LD.RSVCD2.by.mpg|8
40746508|6433A522B6893CB67B9AF4864751C1C4|/
ed2k://|file|Watchmen.2009.TS.Fixed.DivXLTT.avi|1037119472|DF6350D33C98E310DEFE272AEECD3
FB7|/
ed2k://|file|Watchmen.2009.TS.XViDDEViSE.[eMulek.com.pl].avi|1420378078|EF5BE63F98358A3
63989F29D10F9C77E|/
ed2k://|file|Watchmen.(2009).TSScreener.Xvid.Spanish.Lanzam
ientosDivx.es.avi|1439338254|FBAA7FA216FED59C670D351
0B45535A3|/
ed2k://|file|Watchmen.FRENCH.TS..avi|1462099968|D0B5D0
DDA072EE538C5C53E2113E06C3|/
236
Watchmen (Spanish) 2009 TS-Screener
Xvid-Mp3 (Centraldivx).mp4
Watchmen.Die.Waechter.TS.LD.Germa
n.MVCD.for.goldesel.to.mpg
Watchmen.Die.Waechter.TS.LD.MVC
D.shared.for.saugstube.to.mpg
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vi
Watchmen.2009.iTALiAN.MD.TS.Xvi
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Watchmen.REPACK.1CD.FRENCH.TS
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Watchmen(cvcd TorpesTeam)HJ (TsSCREENER)(Xvid)(Spanish)(SpaTorre
nt.com).mpg
Watchmen Die Waechter Ts Ld Rsvcd
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Watchmen.Die.Waechter.TS.LD.RSVC
D.shared.for.saugstube.to.mpg
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ed2k://|file|.Watchmen.(2009).TS.XviD-NL-
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Watchmen [TSScreener][Spanish][2009][www.pctestre
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Completo.avi
2009 Watchmen Director Cut HD.m2ts
Хранители (Watchmen, 2009, 2h42mn,
закадровый) [hdtv hdv hd rus ru]
[AVC(1280x534(2.4),5186(5517)kbps,2
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241
Torrent Links
Torrent Name
Watchmen READNFO TS
XviD DEViSE www
Yestorrent com
Watchmen 2009 iTALiAN
MD TS XviD www
cityofdownloads org
Watchmen TS Xvid Spanish
EspaTorrents com
Watchmen READNFO TS
XviD DEViSE www
cubetorrent com
Torrent Links
http://www.vertor.com/torrents/910820/Watchmen-READNFO-TSXviD-DEViSE-NoRar-curren%3B
http://btjunkie.org/torrent/Watchmen-READNFO-TS-XviD-DEViSErarbg-com/367860cded0bfd0ae1830151fd0da7e01eb79a3bae35
http://www.torrenthound.com/hash/60cded0bfd0ae1830151fd0da7e01eb7
9a3bae35/torrent-info/Watchmen-2009-TS-TELESYNC-XviD-DEViSENoRar%C3%82%C2%A4
http://www.yourbittorrent.com/torrent/5029/Watchmen.READNFO.TS.X
viD.DEViSE.html
http://www.monova.org/details/2428309/WATCHMEN.2009.TSTELESYNC.XVID.DEVISE-NORAR%C2%A4%09.html
http://www.kickasstorrents.com/t2159689.html
http://www.torrentbit.nl/torrent/1660119/Watchmen+READNFO+TS+X
viD-DEViSE-%5Brarbg+com%5D/
http://1337x.org/torrent/14393/0/
http://www.vertor.com/torrents/720218/Watchmen-2009-iTALiAN-MDTS-XviD-volpebianca-volpebianca
http://thepiratebay.org/torrent/4765455/Watchmen.2009.iTALiAN.MD.T
S.XviD.volpebianca[volpebianca]
http://www.h33t.com/details.php?id=cd5ea57b184c25fa1ff904cdc0b0485
33262e79e
http://btjunkie.org/torrent/Watchmen-2009-iTALiAN-MD-TS-XviDvolpebiancavolpebianca/3952cd5ea57b184c25fa1ff904cdc0b048533262e79e
http://www.torrenthound.com/hash/cd5ea57b184c25fa1ff904cdc0b04853
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http://www.monova.org/details/2444062/WATCHMEN%202009%20IT
ALIAN%20MD%20TS%20XVID%20VOLPEBIANCA%5BVOLPEBI
ANCA%5D.html
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265
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