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

Conference paper (2019)

Authors

S. Chockalingam Safety and Security Science - TPM
W. Pieters Safety and Security Science - TPM
André M.H. Teixeira Uppsala University
N. Khakzad Rostami Safety and Security Science - TPM
P.H.A.J.M. van Gelder Safety and Security Science - TPM
Research Group
Safety and Security Science (TPM) (TU Delft)
Copyright: © 2019 S. Chockalingam, W. Pieters, Andre M. H. Teixeira, N. Khakzad, P.H.A.J.M. van Gelder
DOI
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https://doi.org/10.1007/978-3-030-15465-3_3
Safety Security Bayesian Network Fishbone Diagram Intentional Attack Technical Failure
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

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.