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SVAPP methodology: A predictive security vulnerability assessment modeling method


van Staalduinen, MA and Khan, FI and Gadag, V, SVAPP methodology: A predictive security vulnerability assessment modeling method, Journal of Loss Prevention in the Process Industries, 43 pp. 397-413. ISSN 0950-4230 (2016) [Refereed Article]

Copyright Statement

2016 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.jlp.2016.06.017


Recent intentional attacks on the chemical industries in Middle East and Algeria have greatly influenced the risk management mindset. Nominally, probabilistic risk assessment and management has focused on safety and unintentional acts in the chemical and petroleum industry. The focus now needs to be broadened to include intentional acts that will inflict damage on a chemical facility. The proposed Security Vulnerability Assessment, Prevention and Prediction (SVAPP) methodology utilizes an existing safety barrier approach and adapts it to suit the security facet. In total, seven barriers are proposed of which five barriers are utilized to prevent or deter an attack with two overseeing barriers. The five barriers that help deter the security attack are external, internal, interior, critical, and the fail-safe barrier. To reduce the effect of uncertainty in the model, a Bayesian updating technique is proposed along with a predictive capability. This is a key aspect of the model because; with any new information as it accumulates, the model can be updated to better reflect the updated conditions. To illustrate how the model can be executed, a case study is conducted on a figurative liquefied natural gas treating plant. The goal of this work is to raise awareness for the development of security vulnerability assessment related databases in the chemical plants so that they can be used for continually updating the much needed probabilistic security vulnerability assessment in the prevailing environment.

Item Details

Item Type:Refereed Article
Keywords:Bayesian analysis, probability, security risk, threat analysis, vulnerability analysis
Research Division:Engineering
Research Group:Engineering practice and education
Research Field:Risk engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Khan, FI (Professor Faisal Khan)
ID Code:120366
Year Published:2016
Web of Science® Times Cited:13
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-23
Last Modified:2017-11-06

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