Collusion set detection through outlier discovery
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Today's National and Interstate border control agencies are flooded with alerts generated from various monitoring devices. There is an urgent need to uncover potential threats to effectively respond to an event. In this paper, we propose a Semantic Threat Mining approach, to discover threats using the spatio-temporal and semantic relationships among events and data. We represent the potentially dangerous collusion relationships with a Semantic Graph. Using domain-specific ontology of known dangerous relationships, we construct an Enhanced Semantic Graph (ESG) by scoring the edges of the semantic graph and prune it. We further analyze ESG using centrality, cliques and isomorphism to mine the threat patterns. We present a Semantic Threat Mining prototype system in the domain of known dangerous combination of chemicals used in explosives.