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A Bayesian network methodology for optimal security management of critical infrastructures

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  • Misuri, Alessio
  • Khakzad, Nima
  • Reniers, Genserik
  • Cozzani, Valerio

Abstract

Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modelling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.

Suggested Citation

  • Misuri, Alessio & Khakzad, Nima & Reniers, Genserik & Cozzani, Valerio, 2019. "A Bayesian network methodology for optimal security management of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832017311535
    DOI: 10.1016/j.ress.2018.03.028
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    8. Deng, Yu-Jing & Li, Ya-Qian & Qin, Yu-Hua & Dong, Ming-Ru & Liu, Bin, 2020. "Optimal defense resource allocation for attacks in wireless sensor networks based on risk assessment model," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    9. Marroni, Giulia & Casini, Leonardo & Bartolucci, Andrea & Kuipers, Sanneke & Casson Moreno, Valeria & Landucci, Gabriele, 2024. "Development of fragility models for process equipment affected by physical security attacks," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
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