Network Attack Path Selection and Evaluation Based on Q-Learning
As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain pr...
| Published in: | Applied Sciences |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/1/285 |
| _version_ | 1850424769760985088 |
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| author | Runze Wu Jinxin Gong Weiyue Tong Bing Fan |
| author_facet | Runze Wu Jinxin Gong Weiyue Tong Bing Fan |
| author_sort | Runze Wu |
| collection | DOAJ |
| container_title | Applied Sciences |
| description | As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method. |
| format | Article |
| id | doaj-art-0ece56ffce5e4cf189e66d660bc07ded |
| institution | Directory of Open Access Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-0ece56ffce5e4cf189e66d660bc07ded2025-08-19T22:41:11ZengMDPI AGApplied Sciences2076-34172020-12-0111128510.3390/app11010285Network Attack Path Selection and Evaluation Based on Q-LearningRunze Wu0Jinxin Gong1Weiyue Tong2Bing Fan3State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaAs the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.https://www.mdpi.com/2076-3417/11/1/285power CPSdata tampering attackrisk assessmentQ-Learning algorithmFuzzy Petri Net |
| spellingShingle | Runze Wu Jinxin Gong Weiyue Tong Bing Fan Network Attack Path Selection and Evaluation Based on Q-Learning power CPS data tampering attack risk assessment Q-Learning algorithm Fuzzy Petri Net |
| title | Network Attack Path Selection and Evaluation Based on Q-Learning |
| title_full | Network Attack Path Selection and Evaluation Based on Q-Learning |
| title_fullStr | Network Attack Path Selection and Evaluation Based on Q-Learning |
| title_full_unstemmed | Network Attack Path Selection and Evaluation Based on Q-Learning |
| title_short | Network Attack Path Selection and Evaluation Based on Q-Learning |
| title_sort | network attack path selection and evaluation based on q learning |
| topic | power CPS data tampering attack risk assessment Q-Learning algorithm Fuzzy Petri Net |
| url | https://www.mdpi.com/2076-3417/11/1/285 |
| work_keys_str_mv | AT runzewu networkattackpathselectionandevaluationbasedonqlearning AT jinxingong networkattackpathselectionandevaluationbasedonqlearning AT weiyuetong networkattackpathselectionandevaluationbasedonqlearning AT bingfan networkattackpathselectionandevaluationbasedonqlearning |