Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE

False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confin...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 3; s. 943
Hlavní autoři:	Zhao, Siliang, Luo, Wuman, Shu, Qin, Xu, Fangwei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 05.02.2025 MDPI
Témata:	AC state estimation Adaptability controllable false data injection attack data driven Electric power systems extrapolative adversarial variational autoencoder Iran Knowledge Laws, regulations and rules Machine learning Methods Parameter estimation Physics physics informed Iran AC state estimation data driven extrapolative adversarial variational autoencoder controllable false data injection attack physics informed
ISSN:	1424-8220, 1424-8220
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Abstract	False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data distribution of training sets. To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE) for generating controllable and stealthy false data injections. By incorporating physically consistent priors derived from the AC power flow equations, which enforce both the physical laws of power systems and the stealth requirements to evade bad data detection mechanisms, the model learns to generate attack vectors that are physically plausible and stealthy while inducing significant and controllable deviations in state estimation. Experimental results on IEEE-14 and IEEE-118 systems show that the model achieves a 90% success rate in bypassing detection tests for most attack configurations and outperforms methods like SAGAN by generating smoother, more realistic deviations. Furthermore, the use of physical priors enables the model to extrapolate beyond the training data distribution, effectively targeting unseen operational scenarios. This highlights the importance of integrating physics knowledge into data-driven approaches to enhance adaptability and robustness against evolving detection mechanisms.
AbstractList	False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data distribution of training sets. To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE) for generating controllable and stealthy false data injections. By incorporating physically consistent priors derived from the AC power flow equations, which enforce both the physical laws of power systems and the stealth requirements to evade bad data detection mechanisms, the model learns to generate attack vectors that are physically plausible and stealthy while inducing significant and controllable deviations in state estimation. Experimental results on IEEE-14 and IEEE-118 systems show that the model achieves a 90% success rate in bypassing detection tests for most attack configurations and outperforms methods like SAGAN by generating smoother, more realistic deviations. Furthermore, the use of physical priors enables the model to extrapolate beyond the training data distribution, effectively targeting unseen operational scenarios. This highlights the importance of integrating physics knowledge into data-driven approaches to enhance adaptability and robustness against evolving detection mechanisms. False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data distribution of training sets. To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE) for generating controllable and stealthy false data injections. By incorporating physically consistent priors derived from the AC power flow equations, which enforce both the physical laws of power systems and the stealth requirements to evade bad data detection mechanisms, the model learns to generate attack vectors that are physically plausible and stealthy while inducing significant and controllable deviations in state estimation. Experimental results on IEEE-14 and IEEE-118 systems show that the model achieves a 90% success rate in bypassing detection tests for most attack configurations and outperforms methods like SAGAN by generating smoother, more realistic deviations. Furthermore, the use of physical priors enables the model to extrapolate beyond the training data distribution, effectively targeting unseen operational scenarios. This highlights the importance of integrating physics knowledge into data-driven approaches to enhance adaptability and robustness against evolving detection mechanisms.False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data distribution of training sets. To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE) for generating controllable and stealthy false data injections. By incorporating physically consistent priors derived from the AC power flow equations, which enforce both the physical laws of power systems and the stealth requirements to evade bad data detection mechanisms, the model learns to generate attack vectors that are physically plausible and stealthy while inducing significant and controllable deviations in state estimation. Experimental results on IEEE-14 and IEEE-118 systems show that the model achieves a 90% success rate in bypassing detection tests for most attack configurations and outperforms methods like SAGAN by generating smoother, more realistic deviations. Furthermore, the use of physical priors enables the model to extrapolate beyond the training data distribution, effectively targeting unseen operational scenarios. This highlights the importance of integrating physics knowledge into data-driven approaches to enhance adaptability and robustness against evolving detection mechanisms.
Audience	Academic
Author	Xu, Fangwei Luo, Wuman Zhao, Siliang Shu, Qin
AuthorAffiliation	2 School of Applied Sciences, Macao Polytechnic University, Macao, China; luowuman@mpu.edu.mo 1 College of Electrical Engineering, Sichuan University, Chengdu 610000, China; 15913109663@163.com (S.Z.); shuqin@scu.edu.cn (Q.S.)
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Cites_doi	10.1109/TSG.2022.3164874 10.1109/TSP.2014.2385670 10.1109/TSG.2018.2813280 10.1109/TSG.2021.3102329 10.1016/j.segan.2024.101524 10.1109/TPWRS.2015.2504950 10.1109/TSG.2022.3216625 10.1145/1952982.1952995 10.1109/PESGM.2014.6939486 10.1109/TPWRS.2010.2051168 10.1109/TSG.2014.2382714 10.1109/TCYB.2021.3125345 10.1109/TSG.2016.2532347 10.1201/9780203913673 10.1109/PESMG.2013.6672638 10.1109/TPWRD.2009.2028796 10.1109/TII.2024.3390389 10.1109/ACCESS.2020.2988284 10.1145/3575813.3597352 10.1109/TSG.2015.2495133 10.1109/TSG.2017.2708114 10.1109/TPWRS.2018.2818746 10.1109/TSG.2020.3011391 10.1109/TSG.2013.2291661 10.1109/TSG.2020.3033520 10.1109/TII.2021.3129487 10.1109/TII.2024.3374374 10.1109/TSG.2021.3106246 10.1109/JIOT.2022.3147040 10.1109/TSG.2022.3159842 10.1002/sys.21239 10.1109/TSG.2015.2490603
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SubjectTerms	AC state estimation Adaptability controllable false data injection attack data driven Electric power systems extrapolative adversarial variational autoencoder Iran Knowledge Laws, regulations and rules Machine learning Methods Parameter estimation Physics physics informed
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Title	Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE
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