Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms
The integration of large-scale power electronic devices has introduced a large number of strong nonlinear measurement/control nodes into the system, gradually transforming the traditional power system into a cyber physical system (CPS). Many system problems that were originally solved by model-drive...
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| Format: | Article |
| Language: | zho |
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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.1004-9649.202312067 |
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| author | Weiping ZHU Yi TANG Xingshen WEI Zengji LIU |
| author_facet | Weiping ZHU Yi TANG Xingshen WEI Zengji LIU |
| author_sort | Weiping ZHU |
| collection | DOAJ |
| description | The integration of large-scale power electronic devices has introduced a large number of strong nonlinear measurement/control nodes into the system, gradually transforming the traditional power system into a cyber physical system (CPS). Many system problems that were originally solved by model-driven methods have had to be analyzed using data-driven algorithms due to limitations such as dimensional disasters. However, the inherent flaws of data-driven algorithms introduce new risks to the safe and stable operation of the system, which attackers can exploit to launch adversarial attacks that may cause system power outages and even instability. In response to the potential adversarial attacks on data-driven algorithms in power CPS, this paper proposes corresponding defense methods from such three aspects as abnormal data filtering and recovery, algorithm vulnerability mining and optimization, and algorithm self interpretability improvement: abnormal data filter, GAN-based vulnerability mining and optimization method, data knowledge fusion model and its training method. The effectiveness of the proposed method is verified through case analysis. |
| format | Article |
| id | doaj-art-b2e342a776844af4a0bc0af80ac31a22 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-b2e342a776844af4a0bc0af80ac31a222025-08-20T02:56:44ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-09-01579324310.11930/j.issn.1004-9649.202312067zgdl-57-08-zhuweipingDefense Methods for Adversarial Attacks Against Power CPS Data-Driven AlgorithmsWeiping ZHU0Yi TANG1Xingshen WEI2Zengji LIU3State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 211189, ChinaNARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 211189, ChinaThe integration of large-scale power electronic devices has introduced a large number of strong nonlinear measurement/control nodes into the system, gradually transforming the traditional power system into a cyber physical system (CPS). Many system problems that were originally solved by model-driven methods have had to be analyzed using data-driven algorithms due to limitations such as dimensional disasters. However, the inherent flaws of data-driven algorithms introduce new risks to the safe and stable operation of the system, which attackers can exploit to launch adversarial attacks that may cause system power outages and even instability. In response to the potential adversarial attacks on data-driven algorithms in power CPS, this paper proposes corresponding defense methods from such three aspects as abnormal data filtering and recovery, algorithm vulnerability mining and optimization, and algorithm self interpretability improvement: abnormal data filter, GAN-based vulnerability mining and optimization method, data knowledge fusion model and its training method. The effectiveness of the proposed method is verified through case analysis.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202312067adversarial attacksdata-driven algorithmspower cpsattack defense |
| spellingShingle | Weiping ZHU Yi TANG Xingshen WEI Zengji LIU Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms Zhongguo dianli adversarial attacks data-driven algorithms power cps attack defense |
| title | Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms |
| title_full | Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms |
| title_fullStr | Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms |
| title_full_unstemmed | Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms |
| title_short | Defense Methods for Adversarial Attacks Against Power CPS Data-Driven Algorithms |
| title_sort | defense methods for adversarial attacks against power cps data driven algorithms |
| topic | adversarial attacks data-driven algorithms power cps attack defense |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202312067 |
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