Adversarial attack and defense methods for neural network based state estimation in smart grid

Jiwei Tian, Buhong Wang, Jing Li, Charalambos Konstantinou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Deep learning has been recently used in safety-critical cyber-physical systems (CPS) such as the smart grid. The security assessment of such learning-based methods within CPS algorithms, however, is still an open problem. Despite existing research on adversarial attacks against deep learning models, only few works are concerned about safety-critical energy CPS, especially the state estimation routine. This paper investigates security issues of neural network based state estimation in the smart grid. Specifically, the problem of adversarial attacks against neural network based state estimation is analysed and an efficient adversarial attack method is proposed. To thwart this attack, two defense methods based on protection and adversarial training, respectively, are proposed further. The experiments demonstrate that the proposed attack method poses a major threat to neural network based state estimation models. In addition, our results present that defense methods can improve the ability of neural network models to defend against such adversarial attacks.
Original languageEnglish (US)
JournalIET Renewable Power Generation
StatePublished - Nov 21 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-24
Acknowledgements: This work was supported by the National Natural Science Foundation of China (No. 61902426).

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment


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