Detecting Hidden Hierarchy in Terrorist Networks: Some Case Studies
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Practical algorithms for subgroup detection in covert networks
International Journal of Business Intelligence and Data Mining
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
Harvesting covert networks: a case study of the iMiner database
International Journal of Networking and Virtual Organisations
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Data collection is difficult to do in any network analysis because it is hard to create a complete network. It is not easy to gain information on terrorist networks. It is a fact that terrorist organizations do not provide information on their members and the government rarely allows researchers to use their intelligence data [1]. Very few researchers [2] [3] [4] collected data from open sources, and to the best of our knowledge, no knowledge base is available in academia for the analysis of the terrorist events. To counter the information scarcity, we at Software Intelligence Security Research Center, Aalborg University Esbjerg Denmark, designed and developed a terrorism knowledge base by harvesting information from authenticated websites. In this paper we discuss data collection and analysis results of our ongoing research of Investigative Data Mining (IDM). In addition, we present a system architecture of our analyzing, visualizing and destabilizing terrorist networks prototype, i.e., iMiner, and also describe how we collected terrorist information using an Information Harvesting System.