Dark Web: Exploring and Data Mining the Dark Side of the Web by Hsinchun Chen

By Hsinchun Chen

The collage of Arizona man made Intelligence Lab (AI Lab) darkish internet venture is a long term medical examine application that goals to review and comprehend the foreign terrorism (Jihadist) phenomena through a computational, data-centric strategy. We objective to assemble "ALL" web pages generated by way of overseas terrorist teams, together with websites, boards, chat rooms, blogs, social networking websites, movies, digital global, and so on. we now have constructed a variety of multilingual info mining, textual content mining, and internet mining ideas to accomplish hyperlink research, content material research, internet metrics (technical sophistication) research, sentiment research, authorship research, and video research in our learn. The ways and strategies constructed during this undertaking give a contribution to advancing the sector of Intelligence and defense Informatics (ISI). Such advances may also help similar stakeholders to accomplish terrorism learn and facilitate overseas safeguard and peace.

This monograph goals to supply an summary of the darkish internet panorama, recommend a scientific, computational method of knowing the issues, and illustrate with chosen ideas, tools, and case stories constructed via the college of Arizona AI Lab darkish internet crew participants. This paintings goals to supply an interdisciplinary and comprehensible monograph approximately darkish net examine alongside 3 dimensions: methodological concerns in darkish net learn; database and computational recommendations to help details assortment and knowledge mining; and criminal, social, privateness, and knowledge confidentiality demanding situations and methods. it's going to convey valuable wisdom to scientists, safeguard execs, counterterrorism specialists, and coverage makers. The monograph may also function a reference fabric or textbook in graduate point classes with regards to info defense, info coverage, details coverage, info structures, terrorism, and public policy.

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Traditional knowledge discovery techniques include association rules mining, classification and prediction, cluster analysis, and outlier analysis. As natural language processing (NLP) research advances, (multilingual) text mining approaches that automatically extract, summarize, categorize, and translate text documents have also been widely used (Chen 2006). Many of these KDD technologies could be applied in ISI studies. Keeping in mind the special characteristics of crimes, criminals, and security-related data, we categorize existing ISI technologies into six classes: information sharing and collaboration, crime association mining, crime classification and clustering, intelligence text mining, spatial and temporal crime mining, and criminal network mining.

Current research on the technologies for counterterrorism and crime-fighting applications lacks a consistent framework addressing the major challenges. Some information technologies, including data integration, data analysis, text mining, image and video processing, and evidence combination, have been identified as being particularly helpful (National Research Council 2002). However, the question of how to employ them in the intelligence and security domain and use them to effectively address the critical mission areas of national security remains unanswered.

Roberts, and H. Chen, “Developing a Dark Web Collection and Infrastructure for Computational and Social Sciences,” Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics, ISI 2010, Vancouver, Canada, May 2010. • D. Zimbra and H. Chen, “Comparing the Virtual Linkage Intensity and Real World Proximity of Social Movements,” Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics, ISI 2010, Vancouver, Canada, May 2010. • Y.

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