Domino effects are high-impact phenomena that have caused catastrophic damage to several chemical and process plants around the world through secondary incidents caused by primary ones. With the increasing trend of cyberattacks targeting critical infrastructures, there is a concern that such cyberattacks may trigger domino effects, by manipulating industrial control systems in such a way that the physical consequences are likely to escalate. In this study, we have demonstrated that via network segmentation of industrial control systems, the plant robustness against cyberattack-related domino effects can be improved. To this end, a risk-based decision-making methodology is developed based on Bayesian network and graph theory to investigate and evaluate the robustness of segmentation alternatives. The application of the methodology to an illustrative case study shows the efficacy of the approach as a viable cyber risk mitigation measure in chemical and process plants.

Original languageEnglish
Article number102450
Number of pages9
JournalJournal of Information Security and Applications
Volume51
DOIs
Publication statusPublished - 1 Apr 2020

    Research areas

  • Cyber security, Domino effect, Graph theory, Industrial control systems, Process plants, Security by design

ID: 68856966