A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 13
Issue 8
IEEE/CAA Journal of Automatica Sinica
| Citation: | X. Huang and Z. Zhang, “Self-supervised Spatio-temporal contrastive learning for FDIA detection in the smart grid,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1982–1984, Aug. 2026. doi: 10.1109/JAS.2026.125741
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J. Zhao, L. Mili, and M. Wang, “A generalized false data injection attacks against power system nonlinear state estimator and countermeasures,” IEEE Trans. Power Systems, vol. 33, no. 5, pp. 4868–4877, 2018. doi: 10.1109/TPWRS.2018.2794468
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X. Niu, J. Li, J. Sun, and K. Tomsovic, “Dynamic detection of false data injection attack in smart grid using deep learning,” in Proc. IEEE Power & Energy Society Innovative Smart Grid Technologies Conf., 2019, pp. 1–6.
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M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting stealthy false data injection using machine learning in smart grid,” IEEE Systems J., vol. 11, no. 3, pp. 1644–1652, 2014.
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R. Sharma, A. M. Joshi, C. Sahu, and S. J. Nanda, “Detection of false data injection in smart grid using pca based unsupervised learning,” Electrical Engineering, vol. 105, no. 4, pp. 2383–2396, 2023. doi: 10.1007/s00202-023-01809-3
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H. Feng, Y. Han, F. Si, and Q. Zhao, “Detection of false data injection attacks in cyber-physical power systems: An adaptive adversarial dual autoencoder with graph representation learning approach,” IEEE Trans. Instrumentation and Measurement, vol. 73, pp. 1–11, 2024.
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C. Chen, Y. Wang, M. Cui, J. Zhao, W. Bi, Y. Chen, and X. Zhang, “Data-driven detection of stealthy false data injection attack against power system state estimation,” IEEE Trans. Ind. Informatics, vol. 18, no. 12, pp. 8467–8476, 2022. doi: 10.1109/TII.2022.3149106
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P. H. Le-Khac, G. Healy, and A. F. Smeaton, “Contrastive representation learning: A framework and review,” IEEE Access, vol. 8, pp. 193907–193934, 2020. doi: 10.1109/ACCESS.2020.3031549
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J.-B. Grill, F. Strub, F. Altché, et al., “Bootstrap your own latent—A new approach to self-supervised learning,” in Proc. Advances in Neural Information Processing Systems, 2020, vol. 33, pp. 21271–21284.
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