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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.07363 |
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| _version_ | 1866929629234724864 |
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| author | Aftabi, Navid Li, Dan D., Ph. Sharkey, Thomas D, Ph. |
| author_facet | Aftabi, Navid Li, Dan D., Ph. Sharkey, Thomas D, Ph. |
| contents | Industrial Control Systems (ICSs) are widely used in critical infrastructures that face various cyberattacks causing physical damage. With the increasing integration of the ICSs and information technology (IT), ensuring the security of ICSs is of paramount importance. In an ICS, cyberattacks exploit vulnerabilities to compromise sensors and controllers, aiming to cause physical damage. Maliciously accessing different components poses varying risks, highlighting the importance of identifying high-risk cyberattacks. This aids in designing effective detection schemes and mitigation strategies. This paper proposes an optimization-based cyber-risk assessment framework that integrates cyber and physical systems of ICSs. The framework models cyberattacks with varying expertise and knowledge by 1) maximizing physical impact in terms of failure time of the physical system, 2) quickly accessing the sensors and controllers in the cyber system while exploiting limited vulnerabilities, 3) avoiding detection in the physical system, and 4) complying with the cyber and physical restrictions. These objectives enable us to jointly model the interactions between the cyber and physical systems and study the critical cyberattacks that cause the highest impact on the physical system under certain resource constraints. Our framework serves as a tool to understand the vulnerabilities of an ICS with a holistic consideration of cyber and physical systems and their interactions and assess the risk of existing detection schemes by generating the worst-case attack strategies. We illustrate and verify the effectiveness of our proposed method in a numerical and a case study. The results show that a worst-case strategic attacker causes almost 19% further acceleration in the failure time of the physical system while remaining undetected compared to a random attacker. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07363 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An Integrated Cyber-Physical Risk Assessment Framework for Worst-Case Attacks in Industrial Control Systems Aftabi, Navid Li, Dan D., Ph. Sharkey, Thomas D, Ph. Optimization and Control Systems and Control Industrial Control Systems (ICSs) are widely used in critical infrastructures that face various cyberattacks causing physical damage. With the increasing integration of the ICSs and information technology (IT), ensuring the security of ICSs is of paramount importance. In an ICS, cyberattacks exploit vulnerabilities to compromise sensors and controllers, aiming to cause physical damage. Maliciously accessing different components poses varying risks, highlighting the importance of identifying high-risk cyberattacks. This aids in designing effective detection schemes and mitigation strategies. This paper proposes an optimization-based cyber-risk assessment framework that integrates cyber and physical systems of ICSs. The framework models cyberattacks with varying expertise and knowledge by 1) maximizing physical impact in terms of failure time of the physical system, 2) quickly accessing the sensors and controllers in the cyber system while exploiting limited vulnerabilities, 3) avoiding detection in the physical system, and 4) complying with the cyber and physical restrictions. These objectives enable us to jointly model the interactions between the cyber and physical systems and study the critical cyberattacks that cause the highest impact on the physical system under certain resource constraints. Our framework serves as a tool to understand the vulnerabilities of an ICS with a holistic consideration of cyber and physical systems and their interactions and assess the risk of existing detection schemes by generating the worst-case attack strategies. We illustrate and verify the effectiveness of our proposed method in a numerical and a case study. The results show that a worst-case strategic attacker causes almost 19% further acceleration in the failure time of the physical system while remaining undetected compared to a random attacker. |
| title | An Integrated Cyber-Physical Risk Assessment Framework for Worst-Case Attacks in Industrial Control Systems |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2304.07363 |