Understanding the structure of terrorist networks
International Journal of Business Intelligence and Data Mining
Practical algorithms for subgroup detection in covert networks
International Journal of Business Intelligence and Data Mining
Harvesting covert networks: a case study of the iMiner database
International Journal of Networking and Virtual Organisations
Practical algorithms for destabilizing terrorist networks
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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Traditionally most of the literature in social network analysis (SNA) has focused on networks of individuals. Although SNA is not conventionally considered as a data mining technique, it is especially suitable for mining a large volume of association data to discover hidden structural patterns in terrorist networks. After September 11 attacks, SNA has increasingly been used to study terrorist networks. As these covert networks share some features with conventional networks, they are harder to identify because they mask their transactions. The most complicating factor is that terrorist networks are often embedded in a much larger population (i.e., adversaries have links with both covert and innocent individuals). Hence, it is desirable to have tools to correctly classify individuals in covert networks so that the resources for isolating them will be used more efficiently. This paper uses centrality measures from complex networks to discuss how to destabilize adversary networks. We propose newly introduced algorithms for constructing hierarchy of the covert networks, so that investigators can view the structure of the ad hoc networks/ atypical organizations, in order to destabilize the adversaries. The algorithms are also demonstrated by using publicly available dataset. Moreover we also demonstrate techniques for filtering graphs (networks) /detecting particular cells in adversary networks using a fictitious dataset.