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Mitigating cyberattack related domino effects in process plants via ICS segmentation. / Arief, Raditya; Khakzad, Nima; Pieters, Wolter.

In: Journal of Information Security and Applications, Vol. 51, 102450, 01.04.2020.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Arief, R, Khakzad, N & Pieters, W 2020, 'Mitigating cyberattack related domino effects in process plants via ICS segmentation' Journal of Information Security and Applications, vol. 51, 102450. https://doi.org/10.1016/j.jisa.2020.102450

APA

Arief, R., Khakzad, N., & Pieters, W. (2020). Mitigating cyberattack related domino effects in process plants via ICS segmentation. Journal of Information Security and Applications, 51, [102450]. https://doi.org/10.1016/j.jisa.2020.102450

Vancouver

Arief R, Khakzad N, Pieters W. Mitigating cyberattack related domino effects in process plants via ICS segmentation. Journal of Information Security and Applications. 2020 Apr 1;51. 102450. https://doi.org/10.1016/j.jisa.2020.102450

Author

Arief, Raditya ; Khakzad, Nima ; Pieters, Wolter. / Mitigating cyberattack related domino effects in process plants via ICS segmentation. In: Journal of Information Security and Applications. 2020 ; Vol. 51.

BibTeX

@article{d9e6ae41a53c4f82aad2432c347f50d9,
title = "Mitigating cyberattack related domino effects in process plants via ICS segmentation",
abstract = "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.",
keywords = "Cyber security, Domino effect, Graph theory, Industrial control systems, Process plants, Security by design",
author = "Raditya Arief and Nima Khakzad and Wolter Pieters",
year = "2020",
month = "4",
day = "1",
doi = "10.1016/j.jisa.2020.102450",
language = "English",
volume = "51",
journal = "Journal of Information Security and Applications",
issn = "2214-2134",

}

RIS

TY - JOUR

T1 - Mitigating cyberattack related domino effects in process plants via ICS segmentation

AU - Arief, Raditya

AU - Khakzad, Nima

AU - Pieters, Wolter

PY - 2020/4/1

Y1 - 2020/4/1

N2 - 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.

AB - 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.

KW - Cyber security

KW - Domino effect

KW - Graph theory

KW - Industrial control systems

KW - Process plants

KW - Security by design

UR - http://www.scopus.com/inward/record.url?scp=85077808280&partnerID=8YFLogxK

U2 - 10.1016/j.jisa.2020.102450

DO - 10.1016/j.jisa.2020.102450

M3 - Article

VL - 51

JO - Journal of Information Security and Applications

T2 - Journal of Information Security and Applications

JF - Journal of Information Security and Applications

SN - 2214-2134

M1 - 102450

ER -

ID: 68856966