Abstract
With the degree of intelligence increases, smart grid suffers from a large number of attacks from the Internet, and urgently needs a security situation prediction method for active defense. However, traditional methods are usually unable to accurately extract deep features of smart grid, and do not jointly consider spatio-temporal features, resulting in poor situation prediction accuracy. To solve these problems, a SAE+Bi-GRU based security situation prediction algorithm is developed in this paper for smart grid, simply called SBG. Firstly, the stacked auto-encoder (SAE) is used to extract deep spatial features of smart grid at a moment. Secondly, GRU is used to predict the future situation from multiple spatial features extracted by SAE at m consecutive moments. Finally, Bi-GRU is further used to enhance the accuracy of future security situation from two directions. Extensive experiments on the public ORNL dataset prove our algorithm has a better accuracy of situation prediction.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No.62103143); the Hunan Provincial Natural Science Foundation of China (No.2020JJ5199); the National Defense Basic Research Program of China (JCKY2019403D006); and the National Key Research and Development Program (Nos.2019YFE0105300/2019YFE0118700).
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Chen, L., Zheng, M., Liu, Z., Chen, F., Zhou, K., Liu, B. (2022). SAE+Bi-GRU Based Security Situation Prediction for Smart Grid. In: Barolli, L., Kulla, E., Ikeda, M. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-95903-6_3
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DOI: https://doi.org/10.1007/978-3-030-95903-6_3
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