A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications
The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and d...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10742360/ |
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| _version_ | 1846170417558978560 |
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| author | Hana Mejdi Sami Elmadssia Mohamed Koubaa Tahar Ezzedine |
| author_facet | Hana Mejdi Sami Elmadssia Mohamed Koubaa Tahar Ezzedine |
| author_sort | Hana Mejdi |
| collection | DOAJ |
| description | The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques. |
| format | Article |
| id | doaj-art-eb99d718db6d4eae97d85aa5e5aa2b54 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eb99d718db6d4eae97d85aa5e5aa2b542024-11-12T00:01:46ZengIEEEIEEE Access2169-35362024-01-011216363816365310.1109/ACCESS.2024.349150210742360A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning ClassificationsHana Mejdi0https://orcid.org/0000-0002-8623-1568Sami Elmadssia1Mohamed Koubaa2https://orcid.org/0009-0006-1168-0234Tahar Ezzedine3Communication System Laboratory Sys’Com, National Engineering School of Tunis, University of Tunis El Manar, Tunis, TunisiaCommunication System Laboratory Sys’Com, National Engineering School of Tunis, University of Tunis El Manar, Tunis, TunisiaCommunication System Laboratory Sys’Com, National Engineering School of Tunis, University of Tunis El Manar, Tunis, TunisiaCommunication System Laboratory Sys’Com, National Engineering School of Tunis, University of Tunis El Manar, Tunis, TunisiaThe increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.https://ieeexplore.ieee.org/document/10742360/Cyber-physical systemsgame theorysecurityattack modelmachine learningclassification |
| spellingShingle | Hana Mejdi Sami Elmadssia Mohamed Koubaa Tahar Ezzedine A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications IEEE Access Cyber-physical systems game theory security attack model machine learning classification |
| title | A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications |
| title_full | A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications |
| title_fullStr | A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications |
| title_full_unstemmed | A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications |
| title_short | A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications |
| title_sort | comprehensive survey on game theory applications in cyber physical system security attack models security analyses and machine learning classifications |
| topic | Cyber-physical systems game theory security attack model machine learning classification |
| url | https://ieeexplore.ieee.org/document/10742360/ |
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