Learning-based DoS attack game strategy over multi-process systems
In cyber-physical systems, the state information from multiple processes is sent simultaneously to remote estimators through wireless channels. However, with the introduction of open media such as wireless networks, cyber-physical systems may become vulnerable to denial-of-service attacks, which can...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | Mathematical Modelling and Control |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mmc.2024034 |
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| _version_ | 1832590366156521472 |
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| author | Zhiqiang Hang Xiaolin Wang Fangfei Li Yi-ang Ren Haitao Li |
| author_facet | Zhiqiang Hang Xiaolin Wang Fangfei Li Yi-ang Ren Haitao Li |
| author_sort | Zhiqiang Hang |
| collection | DOAJ |
| description | In cyber-physical systems, the state information from multiple processes is sent simultaneously to remote estimators through wireless channels. However, with the introduction of open media such as wireless networks, cyber-physical systems may become vulnerable to denial-of-service attacks, which can pose significant security risks and challenges to the systems. To better understand the impact of denial-of-service attacks on cyber-physical systems and develop corresponding defense strategies, several research papers have explored this issue from various perspectives. However, most current works still face three limitations. First, they only study the optimal strategy from the perspective of one side (either the attacker or defender). Second, these works assume that the attacker possesses complete knowledge of the system's dynamic information. Finally, the power exerted by both the attacker and defender is assumed to be small and discrete. All these limitations are relatively strict and not suitable for practical applications. In this paper, we addressed these limitations by establishing a continuous power game problem of a denial-of-service attack in a multi-process cyber-physical system with asymmetric information. We also introduced the concept of the age of information to comprehensively characterize data freshness. To solve this problem, we employed the multi-agent deep deterministic policy gradient algorithm. Numerical experiments demonstrate that the algorithm is effective for solving the game problem and exhibits convergence in multi-agent environments, outperforming other algorithms. |
| format | Article |
| id | doaj-art-ed446f271e754de6aa53eb2b8b710036 |
| institution | Kabale University |
| issn | 2767-8946 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Modelling and Control |
| spelling | doaj-art-ed446f271e754de6aa53eb2b8b7100362025-01-24T01:02:16ZengAIMS PressMathematical Modelling and Control2767-89462024-12-014442443810.3934/mmc.2024034Learning-based DoS attack game strategy over multi-process systemsZhiqiang Hang0Xiaolin Wang1Fangfei Li2Yi-ang Ren3Haitao Li4School of Mathematics, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mathematics, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mathematics, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mathematics, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mathematics and Statistics, Shandong Normal University, Jinan 250014, ChinaIn cyber-physical systems, the state information from multiple processes is sent simultaneously to remote estimators through wireless channels. However, with the introduction of open media such as wireless networks, cyber-physical systems may become vulnerable to denial-of-service attacks, which can pose significant security risks and challenges to the systems. To better understand the impact of denial-of-service attacks on cyber-physical systems and develop corresponding defense strategies, several research papers have explored this issue from various perspectives. However, most current works still face three limitations. First, they only study the optimal strategy from the perspective of one side (either the attacker or defender). Second, these works assume that the attacker possesses complete knowledge of the system's dynamic information. Finally, the power exerted by both the attacker and defender is assumed to be small and discrete. All these limitations are relatively strict and not suitable for practical applications. In this paper, we addressed these limitations by establishing a continuous power game problem of a denial-of-service attack in a multi-process cyber-physical system with asymmetric information. We also introduced the concept of the age of information to comprehensively characterize data freshness. To solve this problem, we employed the multi-agent deep deterministic policy gradient algorithm. Numerical experiments demonstrate that the algorithm is effective for solving the game problem and exhibits convergence in multi-agent environments, outperforming other algorithms.https://www.aimspress.com/article/doi/10.3934/mmc.2024034cyber-physical systemsgame theorydos attackaoimulti-agent deep deterministic policy gradient |
| spellingShingle | Zhiqiang Hang Xiaolin Wang Fangfei Li Yi-ang Ren Haitao Li Learning-based DoS attack game strategy over multi-process systems Mathematical Modelling and Control cyber-physical systems game theory dos attack aoi multi-agent deep deterministic policy gradient |
| title | Learning-based DoS attack game strategy over multi-process systems |
| title_full | Learning-based DoS attack game strategy over multi-process systems |
| title_fullStr | Learning-based DoS attack game strategy over multi-process systems |
| title_full_unstemmed | Learning-based DoS attack game strategy over multi-process systems |
| title_short | Learning-based DoS attack game strategy over multi-process systems |
| title_sort | learning based dos attack game strategy over multi process systems |
| topic | cyber-physical systems game theory dos attack aoi multi-agent deep deterministic policy gradient |
| url | https://www.aimspress.com/article/doi/10.3934/mmc.2024034 |
| work_keys_str_mv | AT zhiqianghang learningbaseddosattackgamestrategyovermultiprocesssystems AT xiaolinwang learningbaseddosattackgamestrategyovermultiprocesssystems AT fangfeili learningbaseddosattackgamestrategyovermultiprocesssystems AT yiangren learningbaseddosattackgamestrategyovermultiprocesssystems AT haitaoli learningbaseddosattackgamestrategyovermultiprocesssystems |