A CGAN-based adversarial attack method for data-driven state estimation
With the development of data science and technology, the data-driven algorithms under the new power system architecture provide support for real-time and accurate state estimation (SE). However, the adoption of data-driven algorithms also introduces new security risks to power systems due to their i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004260 |
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| author | Qi Wang Jing Zhang Jianxiong Hu Shutan Wu Shiyi Hou Yi Tang |
| author_facet | Qi Wang Jing Zhang Jianxiong Hu Shutan Wu Shiyi Hou Yi Tang |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | With the development of data science and technology, the data-driven algorithms under the new power system architecture provide support for real-time and accurate state estimation (SE). However, the adoption of data-driven algorithms also introduces new security risks to power systems due to their inherent limitations, such as lack of interpretability and poor robustness. Their sensitivity to small perturbations and the limitation of generalization ability make them vulnerable to adversarial attacks. Therefore, to reveal the security threats of adversarial attacks, a conditional generative adversarial network (CGAN) is innovatively adapted to the attack vector generation scenario of the power system in this paper. The feature decoupling and parameter-guided generation capabilities of the model are leveraged to produce adversarial samples that are both covert and threatening. In the proposed CGAN-based adversarial attack method, attackers can execute effective attacks by accessing grid measurements, without requiring any prior knowledge or deep understanding of the system. Simulation results demonstrate that the proposed method could compromise the state estimation process while evading bad data detection, all with reduced attack costs. This enables attackers to manipulate the output of the state estimation model according to their intentions, which represents an improvement over previous work. Comparative analysis with other traditional adversarial attack methods further indicates its effectiveness, thereby uncovering critical security vulnerabilities in data-driven state estimation algorithms. |
| format | Article |
| id | doaj-art-d06c8e96e71245bdad3b9d8d176da444 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-d06c8e96e71245bdad3b9d8d176da4442025-08-20T03:03:46ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011087810.1016/j.ijepes.2025.110878A CGAN-based adversarial attack method for data-driven state estimationQi Wang0Jing Zhang1Jianxiong Hu2Shutan Wu3Shiyi Hou4Yi Tang5Corresponding author at: School of Electrical Engineering, Southeast University, Nanjing 210096, China.; School of Electrical Engineering, Southeast University, Nanjing 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210000, ChinaWith the development of data science and technology, the data-driven algorithms under the new power system architecture provide support for real-time and accurate state estimation (SE). However, the adoption of data-driven algorithms also introduces new security risks to power systems due to their inherent limitations, such as lack of interpretability and poor robustness. Their sensitivity to small perturbations and the limitation of generalization ability make them vulnerable to adversarial attacks. Therefore, to reveal the security threats of adversarial attacks, a conditional generative adversarial network (CGAN) is innovatively adapted to the attack vector generation scenario of the power system in this paper. The feature decoupling and parameter-guided generation capabilities of the model are leveraged to produce adversarial samples that are both covert and threatening. In the proposed CGAN-based adversarial attack method, attackers can execute effective attacks by accessing grid measurements, without requiring any prior knowledge or deep understanding of the system. Simulation results demonstrate that the proposed method could compromise the state estimation process while evading bad data detection, all with reduced attack costs. This enables attackers to manipulate the output of the state estimation model according to their intentions, which represents an improvement over previous work. Comparative analysis with other traditional adversarial attack methods further indicates its effectiveness, thereby uncovering critical security vulnerabilities in data-driven state estimation algorithms.http://www.sciencedirect.com/science/article/pii/S0142061525004260Adversarial attacksConditional generative adversarial networksData-driven algorithmsGenerative adversarial networksState estimation |
| spellingShingle | Qi Wang Jing Zhang Jianxiong Hu Shutan Wu Shiyi Hou Yi Tang A CGAN-based adversarial attack method for data-driven state estimation International Journal of Electrical Power & Energy Systems Adversarial attacks Conditional generative adversarial networks Data-driven algorithms Generative adversarial networks State estimation |
| title | A CGAN-based adversarial attack method for data-driven state estimation |
| title_full | A CGAN-based adversarial attack method for data-driven state estimation |
| title_fullStr | A CGAN-based adversarial attack method for data-driven state estimation |
| title_full_unstemmed | A CGAN-based adversarial attack method for data-driven state estimation |
| title_short | A CGAN-based adversarial attack method for data-driven state estimation |
| title_sort | cgan based adversarial attack method for data driven state estimation |
| topic | Adversarial attacks Conditional generative adversarial networks Data-driven algorithms Generative adversarial networks State estimation |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004260 |
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