Dark Web: Exploring and Data Mining the Dark Side of the Web

ISBN-13: 9781461415565 / Angielski / Twarda / 2011 / 454 str.

Hsinchun Chen

Opis

This book describes the Dark Web landscape of international terrorism, suggests a systematic, computational approach to understanding its problems, and presents techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team.

Recenzje

From the reviews:

"Chen's 450-page monograph is a very detailed (yet understandable), up-to-date account of research into one very specific area of Web research. ... the book can be interesting reading for academicians, researchers, and students at universities ... . It is also recommended for researchers in security-related disciplines. ... the book should also interest security specialists in the industry, especially those dealing with IT-related issues ... . Overall, the book presents a wealth of research results on an important subject, in a consistent way." (P. Navrat, ACM Computing Reviews, October, 2012)

Spis treści

Chapter 1. Dark Web Research Overview.- Chapter 2. Intelligence and Security Informatics (ISI): Research Framework.- Chapter 3. Terrorism Informatics.- Chapter 4. Forum Spidering.- Chapter 5. Link and Content Analysis.- Chapter 6. Dark Network Analysis.- Chapter 7. Interactional Coherence Analysis.- Chapter 8. Dark Web Attribute System.- Chapter 9. Authorship Analysis.- Chapter 10. Sentiment Analysis.- Chapter 11. Affect Analysis.- Chapter 12. Cybergate Visualization.- Chapter 13. Dark Web Forum Portal.- Chapter 14. Jihadi Video Analysis.- Chapter 15. Extremist Youtube Videos.- Chapter 16. Improvised Explosive Devices (IED) on Dark Web.- Chapter 17. Weapons of Mass Destruction (WMD) on Dark Web.- Chapter 18. Bioterrorism Knowledge Mapping.- Chapter 19. Women's Forums on the Dark Web.- Chapter 20. U.S. Domestic Extremist Groups.- Chapter 21. International Falun Gong Movement on the Web.- Chapter 22. Botnets and Cyber Criminals.

O autorze

Hsinchun Chen is McClelland Professor of Management Information Systems (MIS) at the Eller College of the University of Arizona and Andersen Consulting Professor of the Year (1999). He is the author of 15 books and more than 200 articles covering knowledge management, digital library, homeland security, Web computing, and biomedical informatics in leading information technology publications. He serves on ten editorial boards, including: Journal of the American Society for Information Science and Technology, ACM Transactions on Information Systems, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Intelligent Transportation Systems, International Journal of Digital Library, and Decision Support Systems. He has served as a Scientific Advisor/Counselor of the National Library of Medicine (USA), Academia Sinica (Taiwan), and National Library of China (China). Dr. Chen founded The University of Arizona Artificial Intelligence Lab in 1990. The group is distinguished for its applied and high-impact AI research. Since 1990, Dr. Chen has received more than $20M in research funding from various government agencies and major corporations. He has been a PI of the NSF Digital Library Initiative Program and the NIH NLM s Biomedical Informatics Program. His group has developed advanced medical digital library and data and text mining techniques for gene pathway and disease informatics analysis and visualization since 1995. Dr. Chen s nanotechnology patent analysis works, funded by NSF, have been published in the Journal of Nanoparticle Research. His research findings were used in the President s Council of Advisors in Science and Technology s report on "The National Nanotechnology Initiative at Five Years: Assessment and Recommendations of the National Nanotechnology Advisory Panel." Dr. Chen s work also has been recognized by major US corporations and been awarded numerous industry awards for his contribution to IT education and research, including: ATT Foundation Award in Science and Engineering and SAP Award in Research/Applications. Dr. Chen has been heavily involved in fostering digital library, medical informatics, knowledge management, and intelligence informatics research and education in the US and internationally. He has been a PI for more than 20 NSF and NIH research grants since 1990. Dr. Chen is conference chair of ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2004 and has served as the conference general chair or international program committee chair for the past six International Conferences of Asian Digital Libraries (ICADL), 1998-2005. He has been instrumental in fostering the ICADL activities in Asia. Dr. Chen is the founder and also conference co-chair of the IEEE International Conference on Intelligence and Security Informatics (ISI), 2003-2006. The ISI conference has become the premiere meeting for international, national, and homeland security IT research. Dr. Chen is an IEEE fellow.

Blurb

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics (technical sophistication) analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics (ISI). Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace.

This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches.  It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.