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Knowing who to watch: Identifying attackers whose actions are hidden within false alarms and background noise

Author

Listed:
  • Howard Chivers

    (Cranfield University)

  • John A. Clark

    (University of York)

  • Philip Nobles

    (Cranfield University)

  • Siraj A. Shaikh

    (Cranfield University)

  • Hao Chen

    (University of York)

Abstract

Insider attacks are often subtle and slow, or preceded by behavioral indicators such as organizational rule-breaking which provide the potential for early warning of malicious intent; both these cases pose the problem of identifying attacks from limited evidence contained within a large volume of event data collected from multiple sources over a long period. This paper proposes a scalable solution to this problem by maintaining long-term estimates that individuals or nodes are attackers, rather than retaining event data for post-facto analysis. These estimates are then used as triggers for more detailed investigation. We identify essential attributes of event data, allowing the use of a wide range of indicators, and show how to apply Bayesian statistics to maintain incremental estimates without global updating. The paper provides a theoretical account of the process, a worked example, and a discussion of its practical implications. The work includes examples that identify subtle attack behaviour in subverted network nodes, but the process is not network-specific and is capable of integrating evidence from other sources, such as behavioral indicators, document access logs and financial records, in addition to events identified by network monitoring.

Suggested Citation

  • Howard Chivers & John A. Clark & Philip Nobles & Siraj A. Shaikh & Hao Chen, 2013. "Knowing who to watch: Identifying attackers whose actions are hidden within false alarms and background noise," Information Systems Frontiers, Springer, vol. 15(1), pages 17-34, March.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:1:d:10.1007_s10796-010-9268-7
    DOI: 10.1007/s10796-010-9268-7
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    Citations

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    Cited by:

    1. Siraj Ahmed Shaikh & Harsha Kumara Kalutarage, 2016. "Effective network security monitoring: from attribution to target-centric monitoring," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(1), pages 167-178, May.
    2. Carly L. Huth & David W. Chadwick & William R. Claycomb & Ilsun You, 2013. "Guest editorial: A brief overview of data leakage and insider threats," Information Systems Frontiers, Springer, vol. 15(1), pages 1-4, March.
    3. Shuyuan Mary Ho & Merrill Warkentin, 2017. "Leader’s dilemma game: An experimental design for cyber insider threat research," Information Systems Frontiers, Springer, vol. 19(2), pages 377-396, April.
    4. Shuyuan Mary Ho & Merrill Warkentin, 0. "Leader’s dilemma game: An experimental design for cyber insider threat research," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    5. Shaio Yan Huang & Chi-Chen Lin & An-An Chiu & David C. Yen, 2017. "Fraud detection using fraud triangle risk factors," Information Systems Frontiers, Springer, vol. 19(6), pages 1343-1356, December.
    6. Shaio Yan Huang & Chi-Chen Lin & An-An Chiu & David C. Yen, 0. "Fraud detection using fraud triangle risk factors," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    7. Sophie Cockcroft & Mark Russell, 2018. "Big Data Opportunities for Accounting and Finance Practice and Research," Australian Accounting Review, CPA Australia, vol. 28(3), pages 323-333, September.

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