Abstract
Detecting replay attacks in power systems is quite challenging, since the attackers can mimic normal power states and do not make direct damages to the system. Existing works are mostly model-based, which may either suffer from a low detection performance or induce negative side effects to power control. In this paper, we explore purely data-driven approach for good detection performance without side effects. Our basic idea is to learn a classifier using a set of labelled data (i.e., power state) samples to detect the replayed states from normal ones. We choose the Support Vector Machine (SVM) as our classifier, and a self-correlation coefficient as the data feature for detection. We evaluate and confirm the effectiveness of our approach on IEEE bus systems.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Nos. 61502293, 61633016 and 61673255), the Shanghai Young Eastern Scholar Program (No. QD2016030), the Young Teachers’ Training Program for Shanghai College & University, the Science and Technology Commission of Shanghai Municipality (Nos. 17511107002 and 15411953502) and the Shanghai Key Laboratory of Power Station Automation Technology.
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Ma, M., Zhou, P., Du, D., Peng, C., Fei, M., AlBuflasa, H.M. (2017). Detecting Replay Attacks in Power Systems: A Data-Driven Approach. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_45
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DOI: https://doi.org/10.1007/978-981-10-6364-0_45
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