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Combining Bayesian Networks and Fishbone Diagrams to Distinguish Between Intentional Attacks and Accidental Technical Failures

  • Sabarathinam ChockalingamEmail author
  • Wolter Pieters
  • André Teixeira
  • Nima Khakzad
  • Pieter van Gelder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11086)

Abstract

Because of modern societies’ dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

Keywords

Bayesian Network Fishbone diagram Intentional attack Safety Security Technical failure 

Notes

Acknowledgements

This research received funding from the Netherlands Organisation for Scientific Research (NWO) in the framework of the Cyber Security research program under the project “Secure Our Safety: Building Cyber Security for Flood Management (SOS4Flood)”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sabarathinam Chockalingam
    • 1
    Email author
  • Wolter Pieters
    • 1
  • André Teixeira
    • 2
  • Nima Khakzad
    • 1
  • Pieter van Gelder
    • 1
  1. 1.Faculty of Technology, Policy and ManagementDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Engineering SciencesUppsala UniversityUppsalaSweden

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