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Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

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Listed:
  • Tai–ran Wang
  • Vincent Mousseau
  • Nicola Pedroni
  • Enrico Zio

Abstract

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 classification 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 app oaches are here considered 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.

Suggested Citation

  • Tai–ran Wang & Vincent Mousseau & Nicola Pedroni & Enrico Zio, 2015. "Assessing the Performance of a Classification‐Based Vulnerability Analysis Model," Risk Analysis, John Wiley & Sons, vol. 35(9), pages 1674-1689, September.
  • Handle: RePEc:wly:riskan:v:35:y:2015:i:9:p:1674-1689
    DOI: 10.1111/risa.12305
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    References listed on IDEAS

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    4. Aven, T. & Flage, R., 2009. "Use of decision criteria based on expected values to support decision-making in a production assurance and safety setting," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1491-1498.
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    1. Oliva, Gabriele & Faramondi, Luca & Setola, Roberto & Tesei, Marco & Zio, Enrico, 2021. "A multi-criteria model for the security assessment of large-infrastructure construction sites," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).

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