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A State Machine System for Insider Threat Detection

  • Haozhe ZhangEmail author
  • Ioannis Agrafiotis
  • Arnau Erola
  • Sadie Creese
  • Michael Goldsmith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11086)

Abstract

The risk from insider threats is rising significantly, yet the majority of organizations are ill-prepared to detect and mitigate them. Research has focused on providing rule-based detection systems or anomaly detection tools which use features indicative of malicious insider activity. In this paper we propose a system complimentary to the aforementioned approaches. Based on theoretical advances in describing attack patterns for insider activity, we design and validate a state-machine system that can effectively combine policies from rule-based systems and alerts from anomaly detection systems to create attack patterns that insiders follow to execute an attack. We validate the system in terms of effectiveness and scalability by applying it on ten synthetic scenarios. Our results show that the proposed system allows analysts to craft novel attack patterns and detect insider activity while requiring minimum computational time and memory.

Keywords

Insider threat Tripwires Attack patterns 

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haozhe Zhang
    • 1
    Email author
  • Ioannis Agrafiotis
    • 1
  • Arnau Erola
    • 1
  • Sadie Creese
    • 1
  • Michael Goldsmith
    • 1
  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK

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