Computational approaches to suspicion in adversarial settings

David B. Skillicorn ()
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David B. Skillicorn: Queen’s University

Information Systems Frontiers, 2011, vol. 13, issue 1, No 3, 31 pages

Abstract: Abstract Intelligence and law enforcement agencies collect large datasets, but have difficulty focusing analyst attention on the most significant records and structures within them. We address this problem using suspicion, which we interpret as relevant anomaly, as the measure associated with data records and individuals. For datasets collected about widespread activities in which the signs of adversarial activity are rare, we suggest ways to build predictive models of suspicion. For datasets collected as the result of lawful interception, we suggest a model of suspicion spreading using the social network implied by the intercepted data.

Keywords: Lawful interception; Adversarial data analysis; Social networks; Edge prediction; Counterterrorism; Law enforcement; Fraud (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-010-9279-4

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