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Analysis and Computation of Adaptive Defense Strategies Against Advanced Persistent Threats for Cyber-Physical Systems

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11199))

Abstract

Cyber-physical systems are facing new security challenges from Advanced Persistent Threats (APTs) due to the stealthy, dynamic and adaptive nature of the attack. The multi-stage Bayesian game captures the incomplete information of the players’ type, and enables an adaptive belief update according to the observable history of the other player’s actions. The solution concept of perfect Bayesian Nash equilibrium (PBNE) under the proactive and reactive information structures of the players provides an important analytical tool to predict and design the players’ behavior. To capture the learning process and enable fast computation of PBNE, we use conjugate priors to update the beliefs of the players parametrically, which is assimilated into backward dynamic programming with an expanded state space. We use a mathematical programming approach to compute the PBNE of the dynamic bi-matrix game of incomplete information. In the case study, we analyze and study two PBNEs under complete and one-sided incomplete information. The results reveal the benefit of deception of the private attackers’ types and motivate defender’s use of deception techniques to tilt the information asymmetry. Numerical results have been used to corroborate the analytical findings of our framework and show the effectiveness of defense design to deter the attackers and mitigate the APTs strategically.

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Notes

  1. 1.

    Note that \(q^*(\theta _2)=\frac{R_{11}^1(\theta _2)-R_{21}^1(\theta _2)}{R_{22}^1(\theta _2)-R_{12}^1(\theta _2)-R_{21}^1(\theta _2)+R_{11}^1(\theta _2)}, p^*\in [0,1]\) is a equilibrium pair under a restrictive condition \( R_{22}^1(\theta _2)-R_{12}^1(\theta _2)-R_{21}^1(\theta _2)+R_{11}^1(\theta _2)=0, R_{21}^1(\theta _2)=R_{11}^1(\theta _2), \forall \theta _2\).

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Correspondence to Linan Huang or Quanyan Zhu .

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Huang, L., Zhu, Q. (2018). Analysis and Computation of Adaptive Defense Strategies Against Advanced Persistent Threats for Cyber-Physical Systems. In: Bushnell, L., Poovendran, R., Başar, T. (eds) Decision and Game Theory for Security. GameSec 2018. Lecture Notes in Computer Science(), vol 11199. Springer, Cham. https://doi.org/10.1007/978-3-030-01554-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-01554-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01553-4

  • Online ISBN: 978-3-030-01554-1

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