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Game-theoretic modeling and analysis of insider threats

Author

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  • Liu, Debin
  • Wang, XiaoFeng
  • Camp, Jean

Abstract

Malicious insiders are one of the most serious threats to an organization’s information assets. The threat is also extremely difficult to mitigate: an insider can be more knowledgeable than an external attacker about the target system and is, therefore, more effective at defeating security controls that mainly defend against external attacks. A promising technique for addressing the insider threat is to accurately predict an insider’s moves and identify the optimal defense strategy. To this end, we propose a game-theoretic model for the insider problem, which we call an “insider game”. An insider game is built on a stochastic game, a game played in a non-deterministic state machine that can describe most computing systems. The model captures other key properties, especially the system administrator’s uncertainty about the system state due to the insider’s hidden action. The efficacy of the model is demonstrated using a real-life incident. Equilibrium strategies are computed to predict the insider’s actions and identify the best way to respond to them.

Suggested Citation

  • Liu, Debin & Wang, XiaoFeng & Camp, Jean, 2008. "Game-theoretic modeling and analysis of insider threats," International Journal of Critical Infrastructure Protection, Elsevier, vol. 1(C), pages 75-80.
    • Handle: RePEc:eee:ijocip:v:1:y:2008:i:c:p:75-80
    DOI: 10.1016/j.ijcip.2008.08.001
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    Cited by:

    1. Talarico, Luca & Reniers, Genserik & Sörensen, Kenneth & Springael, Johan, 2015. "MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 105-114.
    2. Reniers, Genserik & Soudan, Karel, 2010. "A game-theoretical approach for reciprocal security-related prevention investment decisions," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 1-9.
    3. Wu Jun & Yang Hui & Cheng Yuan, 2015. "Domino Effect Analysis, Assessment and Prevention in Process Industries," Journal of Systems Science and Information, De Gruyter, vol. 3(6), pages 481-498, December.

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