By Hsinchun Chen
The college of Arizona synthetic Intelligence Lab (AI Lab) darkish net undertaking is a long term medical study application that goals to check and comprehend the overseas terrorism (Jihadist) phenomena through a computational, data-centric method. We target to gather "ALL" web pages generated by means of foreign terrorist teams, together with sites, boards, chat rooms, blogs, social networking websites, movies, digital international, and so forth. we've built a variety of multilingual facts mining, textual content mining, and net mining ideas to accomplish hyperlink research, content material research, net metrics (technical sophistication) research, sentiment research, authorship research, and video research in our examine. The ways and strategies built during this undertaking give a contribution to advancing the sphere of Intelligence and safeguard Informatics (ISI). Such advances can assist comparable stakeholders to accomplish terrorism study and facilitate foreign defense and peace.
This monograph goals to supply an outline of the darkish net panorama, recommend a scientific, computational method of figuring out the issues, and illustrate with chosen recommendations, equipment, and case reports built via the collage of Arizona AI Lab darkish internet crew individuals. This paintings goals to supply an interdisciplinary and comprehensible monograph approximately darkish net examine alongside 3 dimensions: methodological matters in darkish net study; database and computational suggestions to help details assortment and knowledge mining; and criminal, social, privateness, and information confidentiality demanding situations and ways. it's going to carry important wisdom to scientists, safety execs, counterterrorism specialists, and coverage makers. The monograph may also function a reference fabric or textbook in graduate point classes relating to info safeguard, details coverage, info coverage, details platforms, terrorism, and public policy.
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Additional resources for Dark Web: Exploring and Data Mining the Dark Side of the Web
And Uthurusamy, R. (2002). Evolving data mining into solutions for insights. Communications of the ACM, 45(8), 28–31. National Research Council. (2002). Making the Nation Safer: The Role of Science and Technology in Countering Terrorism. Washington, DC: National Academy Press. O’Harrow, R. (2005). No Place to Hide. New York: Free Press. Office of Homeland Security. (2002). National Strategy for Homeland Security. : Office of Homeland Security. Sageman, M. (2004). Understanding Terror Networks. Philadelphia: University of Pennsylvania Press.
Chen, H. Rolka, and B. ), Biosurveillance and BioSecurity, International Workshop, BioSecure 2008, Springer-Verlag, December 2008. • H. Chen and C. ), Intelligence and Security Informatics: Techniques and Applications, Springer, 2008. • H. Chen, E. Reid, J. Sinai, A. Silke, and B. ), Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security, Springer, 2008. • H. Chen, T. S. Raghu, R. Ramesh, A. Vinze, and D. ), Handbooks in Information Systems – National Security, Elsevier Scientific, 2007.
A few readers and reporters have cautioned about potential misuse of Dark Web contents by government agencies and authorities. The Dark Web project is unlike Total Information Awareness (TIA). This is not a secretive government project conducted by spooks. We perform scientific, longitudinal, hypothesis-guided terrorism research like other terrorism researchers. However, we are clearly more computationally oriented, unlike other traditional terrorism research that relies on sociology, communications, and policy-based methodologies.
Dark Web: Exploring and Data Mining the Dark Side of the Web by Hsinchun Chen