False data injection attack in smart grid: Attack model and reinforcement learning-based detection method
The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of at...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1104989/full |
| _version_ | 1828055791608791040 |
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| author | Xixiang Lin Dou An Feifei Cui Feiye Zhang |
| author_facet | Xixiang Lin Dou An Feifei Cui Feiye Zhang |
| author_sort | Xixiang Lin |
| collection | DOAJ |
| description | The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios. |
| first_indexed | 2024-04-10T20:44:07Z |
| format | Article |
| id | doaj.art-cf5f8d35a46144f892e3e5f59025bc1c |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T20:44:07Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-cf5f8d35a46144f892e3e5f59025bc1c2023-01-24T11:49:27ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.11049891104989False data injection attack in smart grid: Attack model and reinforcement learning-based detection methodXixiang LinDou AnFeifei CuiFeiye ZhangThe smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1104989/fullstate estimationdeep reinforcement learningattack detectionsmart gridfalse data injection attack |
| spellingShingle | Xixiang Lin Dou An Feifei Cui Feiye Zhang False data injection attack in smart grid: Attack model and reinforcement learning-based detection method Frontiers in Energy Research state estimation deep reinforcement learning attack detection smart grid false data injection attack |
| title | False data injection attack in smart grid: Attack model and reinforcement learning-based detection method |
| title_full | False data injection attack in smart grid: Attack model and reinforcement learning-based detection method |
| title_fullStr | False data injection attack in smart grid: Attack model and reinforcement learning-based detection method |
| title_full_unstemmed | False data injection attack in smart grid: Attack model and reinforcement learning-based detection method |
| title_short | False data injection attack in smart grid: Attack model and reinforcement learning-based detection method |
| title_sort | false data injection attack in smart grid attack model and reinforcement learning based detection method |
| topic | state estimation deep reinforcement learning attack detection smart grid false data injection attack |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1104989/full |
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