Development of fragility models for process equipment affected by physical security attacks

Giulia Marroni, Leonardo Casini, Andrea Bartolucci, Sanneke Kuipers, Valeria Casson Moreno and Gabriele Landucci

Reliability Engineering and System Safety, 2024, vol. 243, issue C

Abstract: The vulnerability of chemical and process facilities toward physical security attacks depends on the equipment resistance against such attacks and on the performance of Physical Protection Systems (PPS) in place. To enhance the protection against intentional attacks, the development of quantitative vulnerability metrics is essential, nevertheless current standard approaches only offer qualitative or semi-quantitative evaluations. The aim of the present work is to develop a quantitative methodology for the assessment of chemical and process facilities vulnerability towards external acts of interference. The proposed methodology is based both on the evaluation of equipment structural integrity in response to different types of specific impact vectors characterizing intentional attacks and on the quantitative performance assessment of related PPS. In particular, specific fragility models were developed for impact vectors associated with improvised explosive devices, firearms, and incendiary weapons. The novel fragility models were implemented in a comprehensive security vulnerability assessment (SVA) based on Bayesian Networks, in which the contribution of PPS performance was also considered. A case study was defined and analyzed to exemplify the application of the proposed approach. The results obtained allowed for the identification of the most critical security-related escalation scenarios and thus for an improved quantitative SVA.

Keywords: Security Vulnerability Assessment; Bayesian Networks; Fragility models; Major accident hazard; Security management; Physical Protection System (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007949

DOI: 10.1016/j.ress.2023.109880

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