000 04594nam a22004935i 4500
001 978-1-4614-1557-2
003 DE-He213
005 20140220083243.0
007 cr nn 008mamaa
008 111216s2012 xxu| s |||| 0|eng d
020 _a9781461415572
_9978-1-4614-1557-2
024 7 _a10.1007/978-1-4614-1557-2
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aChen, Hsinchun.
_eauthor.
245 1 0 _aDark Web
_h[electronic resource] :
_bExploring and Data Mining the Dark Side of the Web /
_cby Hsinchun Chen.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2012.
300 _aXXVI, 451p. 150 illus., 81 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntegrated Series in Information Systems,
_x1571-0270 ;
_v30
505 0 _aChapter 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.
520 _aThe 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.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aOperations research.
650 0 _aManagement information systems.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aBusiness Information Systems.
650 2 4 _aOperation Research/Decision Theory.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461415565
830 0 _aIntegrated Series in Information Systems,
_x1571-0270 ;
_v30
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-1557-2
912 _aZDB-2-SBE
999 _c101095
_d101095