Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning
Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9594 |
| _version_ | 1827655342157201408 |
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| author | Yiqun Yue Yang Zhou Lijuan Xu Dawei Zhao |
| author_facet | Yiqun Yue Yang Zhou Lijuan Xu Dawei Zhao |
| author_sort | Yiqun Yue |
| collection | DOAJ |
| description | Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms. |
| first_indexed | 2024-03-09T22:05:24Z |
| format | Article |
| id | doaj.art-42c00d6434a0424597750ba9b6c6cf5b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:24Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-42c00d6434a0424597750ba9b6c6cf5b2023-11-23T19:42:17ZengMDPI AGApplied Sciences2076-34172022-09-011219959410.3390/app12199594Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based LearningYiqun Yue0Yang Zhou1Lijuan Xu2Dawei Zhao3Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaIndustrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms.https://www.mdpi.com/2076-3417/12/19/9594industrial control systemsoptimal protection strategyreinforcement learningdifferential evolution algorithmsopposition-based learning |
| spellingShingle | Yiqun Yue Yang Zhou Lijuan Xu Dawei Zhao Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning Applied Sciences industrial control systems optimal protection strategy reinforcement learning differential evolution algorithms opposition-based learning |
| title | Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning |
| title_full | Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning |
| title_fullStr | Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning |
| title_full_unstemmed | Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning |
| title_short | Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning |
| title_sort | optimal defense strategy selection algorithm based on reinforcement learning and opposition based learning |
| topic | industrial control systems optimal protection strategy reinforcement learning differential evolution algorithms opposition-based learning |
| url | https://www.mdpi.com/2076-3417/12/19/9594 |
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