Identification of protective actions to reduce the vulnerability of safety-critical systems to malevolent acts: a sensitivity-based decision-making approach

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.
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  • HAL Id : hal-01270677, version 1

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Tairan Wang, Nicola Pedroni, Enrico Zio. Identification of protective actions to reduce the vulnerability of safety-critical systems to malevolent acts: a sensitivity-based decision-making approach. Reliability Engineering and System Safety, Elsevier, 2016, 147, pp.9-18. ⟨hal-01270677⟩

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