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Computational approaches to suspicion in adversarial settings

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  • David B. Skillicorn

    (Queen’s University)

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.

Suggested Citation

  • David B. Skillicorn, 2011. "Computational approaches to suspicion in adversarial settings," Information Systems Frontiers, Springer, vol. 13(1), pages 21-31, March.
    • Handle: RePEc:spr:infosf:v:13:y:2011:i:1:d:10.1007_s10796-010-9279-4
    DOI: 10.1007/s10796-010-9279-4
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    References listed on IDEAS

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    1. Pietronero, L. & Tosatti, E. & Tosatti, V. & Vespignani, A., 2001. "Explaining the uneven distribution of numbers in nature: the laws of Benford and Zipf," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 293(1), pages 297-304.
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    Cited by:

    1. Goldstone, Jack A. (Голдстоун, Джек) & Korotaev, Andrey (Коротаев, Андрей) & Zinkina, Yulia (Зинькина, Юлия), 2015. "Political Demography of the World Economy: Tropical Africa [Политическая Демография Мировой Экономики: Страны Тропической Африки]," Published Papers mn45, Russian Presidential Academy of National Economy and Public Administration.
    2. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    3. Hsinchun Chen & Yilu Zhou & Edna F. Reid & Catherine A. Larson, 2011. "Introduction to special issue on terrorism informatics," Information Systems Frontiers, Springer, vol. 13(1), pages 1-3, March.
    4. Yanxin Wang & Jian Li & Xi Zhao & Gengzhong Feng & Xin (Robert) Luo, 2020. "Using Mobile Phone Data for Emergency Management: a Systematic Literature Review," Information Systems Frontiers, Springer, vol. 22(6), pages 1539-1559, December.

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