Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
COPLINK: managing law enforcement data and knowledge
Communications of the ACM
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
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It is well recognized that advanced filtering and mining in information streams and intelligence bases are of key importance in investigative analysis for countering terrorism and organized crime. As opposed to traditional data mining aiming at extracting knowledge form data, mining for investigative analysis, called Investigative Data Mining (IDM), aims at discovering hidden instances of patterns of interest, such as patterns indicating an organized crime activity. An important problem targeted by IDM is identification of terror/crime networks, based on available intelligence and other information. We present an approach to an IDM solution of this problem, using semantic link analysis and visualization of findings. The approach is demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist and is capable of determining links between terrorism plots occurred in the past, their affiliation with terrorist camps, travel record, and funds transfer, etc. The findings are represented by a network in the form of an attributed relational graph. Paths from a node to any other node in the network indicate the relationships between individuals and organizations. The system also provides assistance to law enforcement agencies, indicating when the capture of a specific terrorist will likely destabilize the terrorist network.