Development of the framework for quantitative cyber risk assessment in nuclear facilities
Industrial control systems in nuclear facilities are facing increasing cyber threats due to the widespread use of information and communication equipment. To implement cyber security programs effectively through the RG 5.71, it is necessary to quantitatively assess cyber risks. However, this can be...
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Published in | Nuclear engineering and technology Vol. 55; no. 6; pp. 2034 - 2046 |
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Main Authors | , , |
Format | Journal Article |
Language | Korean |
Published |
2023
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Subjects | |
Online Access | Get full text |
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Summary: | Industrial control systems in nuclear facilities are facing increasing cyber threats due to the widespread use of information and communication equipment. To implement cyber security programs effectively through the RG 5.71, it is necessary to quantitatively assess cyber risks. However, this can be challenging due to limited historical data on threats and customized Critical Digital Assets (CDAs) in nuclear facilities. Previous works have focused on identifying data flows, the assets where the data is stored and processed, which means that the methods are heavily biased towards information security concerns. Additionally, in nuclear facilities, cyber threats need to be analyzed from a safety perspective. In this study, we use the system theoretic process analysis to identify system-level threat scenarios that could violate safety constraints. Instead of quantifying the likelihood of exploiting vulnerabilities, we quantify Security Control Measures (SCMs) against the identified threat scenarios. We classify the system and CDAs into four consequence-based classes, as presented in NEI 13-10, to analyze the adversary impact on CDAs. This allows for the ranking of identified threat scenarios according to the quantified SCMs. The proposed framework enables stakeholders to more effectively and accurately rank cyber risks, as well as establish security and response strategies. |
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Bibliography: | KISTI1.1003/JNL.JAKO202320251457403 |
ISSN: | 1738-5733 2234-358X |