Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach

Abstract

An empirical classification model based on the Majority Rule Sorting (MR‐Sort) method has been previously proposed by the authors to evaluate the vulnerability of safety‐critical systems (in particular, nuclear power plants [NPPs]) with respect to malevolent intentional acts. In this article, the model serves as the basis for an analysis aimed at determining a set of protective actions to be taken (e.g., increasing the number of monitoring devices, reducing the number of accesses to the safety‐critical system) in order to effectively reduce the level of vulnerability of the safety‐critical systems under consideration. In particular, the problem is here tackled within an optimization framework: the set of protective actions to implement is chosen as the one minimizing the overall level of vulnerability of a group of safety‐critical systems. In this context, three different optimization approaches have been explored: (i) one single classification model is built to evaluate and minimize system vulnerability; (ii) an ensemble of compatible classification models, generated by the bootstrap method, is employed to perform a “robust” optimization, taking as reference the “worst‐case” scenario over the group of models; (iii) finally, a distribution of classification models, still obtained by bootstrap, is considered to address vulnerability reduction in a “probabilistic” fashion (i.e., by minimizing the “expected” vulnerability of a fleet of systems). The results are presented and compared with reference to a fictitious example considering NPPs as the safety‐critical systems of interest.

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