Assessing the Performance of a Classification-Based Vulnerability Analysis Model
Résumé
In this article, a classification model based on the majority rule sorting (MR-Sort) method is employed to evaluate the vulnerability of safety-critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited-size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the clas-sification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different approaches are here con-sidered to this aim: (i) a model-retrieval-based approach, (ii) the bootstrap method, and (iii) the leave-one-out cross-validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.
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