A Bayesian-network approach for assessing the probability of success of physical security attacks to offshore Oil&Gas facilities. (1st April 2023)

• Record Type: Journal Article
• Authors: Iaiani, Matteo; Tugnoli, Alessandro; Cozzani, Valerio; Reniers, Genserik; Yang, Ming
• Is Part Of: Ocean engineering. Volume 273(2023)
• Publication Date: 2023-04-01
• Subjects: Security -- Security attack -- Offshore Oil&Gas industry -- Bayesian network -- Security risk -- Quantitative assessment; ASD Adversary Sequence Diagram -- ATT Adversary Task Time -- ATTr Adversary Task Time remaining after the first detection -- BN Bayesian Network -- CPT Conditional Probability Table -- DAG Directed Acyclic Graph -- EASI Estimate of Adversary Sequence Interruption -- ESD Emergency Shutdown -- PPS Physical Protection System -- RT Response Time -- SD Standard Deviation -- SIT Security Intervention Time -- SRA Security Risk Assessment -- SVA Security Vulnerability Assessment; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.114010
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Offshore Oil&Gas facilities are attractive targets of intentional malicious attacks (security attacks) that may trigger cascading events (e.g., the release and dispersion of hazardous material and/or energy, fires, explosions) with consequences on people, environment, and assets. The severity of these consequences is potentially similar to those arising from major accident scenarios originated by conventional safety-related causes. Current practice in managing the risk of security attacks mostly relies on qualitative or semi-quantitative procedures developed over the years in the offshore Oil&Gas industry. In the present study, a systematic quantitative procedure is developed, based on a Bayesian Network (BN) approach, for calculating the probability of success of physical security attacks, taking into account both preventive and mitigative security intervention strategies. The procedure addresses the specific framework of the offshore Oil&Gas industry. A case study concerning an offshore fixed Oil&Gas platform allowed us to demonstrate the quality of the results that can be achieved and their potential towards the improvement of the security of the installations considered. Highlights: A systematic quantitative procedure based on Bayesian Network is proposed. The procedure is aimed at calculating the probability of security attack success. The procedure was tested on a case study addressing an offshore Oil&Gas platform. Detection in the sea is fundamental in security attacks prevention and mitigation. The results support the vulnerability assessment phase of SVA/SRA methods.