By Hsinchun Chen
The collage of Arizona synthetic Intelligence Lab (AI Lab) darkish net venture is a long term medical examine application that goals to check and comprehend the overseas terrorism (Jihadist) phenomena through a computational, data-centric procedure. We objective to assemble "ALL" websites generated by means of overseas terrorist teams, together with sites, boards, chat rooms, blogs, social networking websites, video clips, digital global, and so on. we've constructed quite a few multilingual facts mining, textual content mining, and net mining suggestions to accomplish hyperlink research, content material research, internet metrics (technical sophistication) research, sentiment research, authorship research, and video research in our learn. The methods and techniques constructed during this venture give a contribution to advancing the sphere of Intelligence and safety Informatics (ISI). Such advances can assist comparable stakeholders to accomplish terrorism examine and facilitate foreign safety and peace. This monograph goals to supply an outline of the darkish internet panorama, recommend a scientific, computational method of knowing the issues, and illustrate with chosen recommendations, equipment, and case stories built by means of the collage of Arizona AI Lab darkish net staff individuals. This paintings goals to supply an interdisciplinary and comprehensible monograph approximately darkish internet study alongside 3 dimensions: methodological matters in darkish net learn; database and computational innovations to aid details assortment and knowledge mining; and criminal, social, privateness, and knowledge confidentiality demanding situations and approaches. it's going to carry worthwhile wisdom to scientists, protection pros, counterterrorism specialists, and coverage makers. The monograph can additionally function a reference fabric or textbook in graduate point classes with regards to info safety, details coverage, details coverage, details structures, terrorism, and public coverage.
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Additional info for Dark Web: Exploring and Data Mining the Dark Side of the Web (Integrated Series in Information Systems)
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Yang, M. Chau, J. Wang, and H. ), “Security Informatics,” Annals of Information Systems, Springer, 2010. • H. Chen, M. Dacier, et al. ), Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, Paris, France, June 2009. • D. Zeng, L. Khan, L. Zhou, M. Day, C. Yang, B. Thuraisingham, and H. ), Proceedings of the 2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009, Dallas, Texas, June 2009. • H. Chen, C. Yang, M. Chau, and S. ), Intelligence and Security Informatics, Proceedings of the Pacific-Asia Workshop, PAISI 2009, Bangkok, Thailand, Lecture Notes in Computer Science (LNCS 5477), Springer-Verlag, 2009.
Hu, and H. Chen, “Dynamics of Terrorist Networks,” Journal of Homeland Security and Emergency Management, forthcoming, 2010. • A. Abbasi, H. Chen, and Z. Zhang, “Selecting Attributes for Sentiment Classification using Feature Relation Networks,” IEEE Transactions on Knowledge and Data Engineering, forthcoming, 2010. • T. J. Fu, A. Abbasi, and H. Chen, “A Focused Crawler for Dark Web Forums,” Journal of the American Society for Information Science and Technology, Volume 61, Number 6, Pages 1213–1231, 2010.