Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network
Phasor measurement unit (PMU) plays a crucial role in smart grids, enabling precise synchronized acquisition of electric power data. Due to the use of the global positioning system (GPS) for time synchronization, the PMU is vulnerable to GPS spoofing attack (GSA), which impacts the normal data acqui...
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| Format: | Article |
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State Grid Energy Research Institute
2024-09-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311025 |
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| author | Hui WU Ziwei ZOU Fengming XIAO Jie LIU Chenpeng MIN Zhuoqun XIA |
| author_facet | Hui WU Ziwei ZOU Fengming XIAO Jie LIU Chenpeng MIN Zhuoqun XIA |
| author_sort | Hui WU |
| collection | DOAJ |
| description | Phasor measurement unit (PMU) plays a crucial role in smart grids, enabling precise synchronized acquisition of electric power data. Due to the use of the global positioning system (GPS) for time synchronization, the PMU is vulnerable to GPS spoofing attack (GSA), which impacts the normal data acquisition. The existing GSA defense methods have low restoration accuracy and require additional hardware costs. To address the aforementioned issues, this paper proposes a GSA defense method based on bidirectional long short-term memory (BiLSTM) network and self-attention mechanism generative adversarial network. Firstly, an improved WGAN-GP model is proposed to redesign the network architecture of the generator and discriminator, and the BiLSTM network and self-attention mechanism are incorporated into the generator and discriminator to enhance the model's generative performance and discriminative ability. Secondly, based on the proposed WGAN-GP model, a GSA defense model is constructed, which includes two crucial modules: an attack detection network and a data restoration network that are employed to detect the smart grid GSA and repair the compromised PMU measurement data, respectively. Finally, We simulated GSA attacks in the IEEE-39 bus system and validated the effectiveness of the proposed method on the corresponding dataset. The results show that compared to existing methods, the proposed approach outperforms in most performance indicators. |
| format | Article |
| id | doaj-art-17c423c6fc0149f98c9ed1d4a2ef0fbb |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-17c423c6fc0149f98c9ed1d4a2ef0fbb2025-08-20T02:04:31ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-09-01579617010.11930/j.issn.1004-9649.202311025zgdl-57-06-wuhuiDefense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial NetworkHui WU0Ziwei ZOU1Fengming XIAO2Jie LIU3Chenpeng MIN4Zhuoqun XIA5Wuling Electric Power Co., Ltd., Changsha 410004, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaWuling Electric Power Co., Ltd., Changsha 410004, ChinaWuling Electric Power Co., Ltd., Changsha 410004, ChinaWuling Electric Power Co., Ltd., Changsha 410004, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410076, ChinaPhasor measurement unit (PMU) plays a crucial role in smart grids, enabling precise synchronized acquisition of electric power data. Due to the use of the global positioning system (GPS) for time synchronization, the PMU is vulnerable to GPS spoofing attack (GSA), which impacts the normal data acquisition. The existing GSA defense methods have low restoration accuracy and require additional hardware costs. To address the aforementioned issues, this paper proposes a GSA defense method based on bidirectional long short-term memory (BiLSTM) network and self-attention mechanism generative adversarial network. Firstly, an improved WGAN-GP model is proposed to redesign the network architecture of the generator and discriminator, and the BiLSTM network and self-attention mechanism are incorporated into the generator and discriminator to enhance the model's generative performance and discriminative ability. Secondly, based on the proposed WGAN-GP model, a GSA defense model is constructed, which includes two crucial modules: an attack detection network and a data restoration network that are employed to detect the smart grid GSA and repair the compromised PMU measurement data, respectively. Finally, We simulated GSA attacks in the IEEE-39 bus system and validated the effectiveness of the proposed method on the corresponding dataset. The results show that compared to existing methods, the proposed approach outperforms in most performance indicators.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311025pmuattack detectiondata repairgsawgan-gp model |
| spellingShingle | Hui WU Ziwei ZOU Fengming XIAO Jie LIU Chenpeng MIN Zhuoqun XIA Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network Zhongguo dianli pmu attack detection data repair gsa wgan-gp model |
| title | Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network |
| title_full | Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network |
| title_fullStr | Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network |
| title_full_unstemmed | Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network |
| title_short | Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network |
| title_sort | defense method for smart grid gps spoofing attack based on bilstm and self attention mechanism generative adversarial network |
| topic | pmu attack detection data repair gsa wgan-gp model |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311025 |
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