Identification of protective actions to reduce the vulnerability of safety-critical systems to malevolent acts: A sensitivity-based decision-making approach. (March 2016)

• Record Type: Journal Article
• Authors: Wang, Tai-Ran; Pedroni, Nicola; Zio, Enrico
• Is Part Of: Reliability engineering & system safety. Volume 147(2016:Mar.)
• Page Start: 9
• Page End: 18
• Publication Date: 2016-03
• Subjects: Safety-critical system -- Malevolent intentional attacks -- Vulnerability analysis -- Protective actions -- Majority Rule Sorting (MR-Sort) -- Classification model -- Inverse classification problem -- Sensitivity indicator; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2015.09.005
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: A classification model based on the Majority Rule Sorting method has been previously proposed by the authors to evaluate the vulnerability of safety-critical systems (e.g., nuclear power plants) with respect to malevolent intentional acts. In this paper, we consider a classification model previously proposed by the authors based on the Majority Rule Sorting method to evaluate the vulnerability of safety-critical systems (e.g., nuclear power plants) with respect to malevolent intentional acts. The model is here used as the basis for solving an inverse classification problem aimed at determining a set of protective actions to reduce the level of vulnerability of the safety-critical system under consideration. To guide the choice of the set of protective actions, sensitivity indicators are originally introduced as measures of the variation in the vulnerability class that a safety-critical system is expected to undergo after the application of a given set of protective actions. These indicators form the basis of an algorithm to rank different combinations of actions according to their effectiveness in reducing the safety-critical systems vulnerability. Results obtained using these indicators are presented with regard to the application of: (i) one identified action at a time, (ii) all identified actions at the same time or (iii) a random combination of identified actions. The results are presented with reference to a fictitious example considering nuclear power plants as the safety-critical systems object of the analysis. Highlights: We use a hierarchical framework to represent the vulnerability. We use an empirical classification model to evaluate vulnerability. Sensitivity indicators are introduced to rank protective actions. Constraints (e.g., budget limitations) are accounted for. Method is applied to fictitious Nuclear Power Plants.