Investigative data mining for counterterrorism

Authors:
Muhammad Akram Shaikh;Jiaxin Wang;Hongbo Liu;Yixu Song
Affiliations:
State Key Lab of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, P.R. China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, P.R. China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, P.R. China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, P.R. China
Venue:
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Year:
2006

Citing 4
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Abstract

After the tragic events of 9/11, the concern about national security has increased significantly. However, law enforcement agencies, particularly in view of current emphasis on terrorism, increasingly face the challenge of information overload and lack of advanced, automated techniques for the effective analysis of criminal and terrorism activities. Data mining applied in the context of law enforcement and intelligence analysis, called Investigative Data Mining (IDM), holds the promise of alleviating such problems. An important problem targeted by IDM is the identification of terror/crime networks, based on available intelligence and other information. In this paper, we present an understanding to show how IDM works and the importance of this approach in the context of terrorist network investigations and give particular emphasis on how to destabilize them by knowing the information about leaders and subgroups through hierarchical structure.