Abnormal flow detection in industrial control network based on deep 
reinforcement learning

Wang, Weiping; Guo, Junjiang; Wang, Zhen; Wang, Hao; Cheng, Jun; Wang, Chunyang; Yuan, Manman; Kurths, Jürgen; Luo, Xiong; Gao, Yang

doi:10.1016/j.amc.2021.126379

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Abnormal flow detection in industrial control network based on deep reinforcement learning

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Wang, Weiping
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Guo, Junjiang
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Wang, Zhen
External Organizations;

Wang, Hao
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Cheng, Jun
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Wang, Chunyang
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Yuan, Manman
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/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Luo, Xiong
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Gao, Yang
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Citation

Wang, W., Guo, J., Wang, Z., Wang, H., Cheng, J., Wang, C., Yuan, M., Kurths, J., Luo, X., Gao, Y. (2021): Abnormal flow detection in industrial control network based on deep reinforcement learning. - Applied Mathematics and Computation, 409, 126379.
https://doi.org/10.1016/j.amc.2021.126379

Cite as: https://publications.pik-potsdam.de/pubman/item/item_26268

Abstract

Industrial control systems are the brain and central nervous system of a country’s vital infrastructure. Once the control system collapses, the consequences are unimaginable. Therefore, the safety of industrial control system has become the top priority in the field of safety. Aiming at the problem that the traditional abnormal flow detection model in the industrial control system is not accurate in identifying abnormalities, we combine the perception ability of deep learning with the decision-making ability of reinforcement learning, and propose an abnormal flow detection model based on deep reinforcement learning. The neural network is used to extract the features of the preprocessed dataset, and then the learning strategy can be adjusted according to the special advantages of strengthening the decision-making ability of learning and feedback. The experimental results show that the model based on deep reinforcement learning can achieve 98.06% accuracy in abnormal flow detection.Compared with various methods proposed by peers in current literature, this method is superior to other technologies in four evaluation indexes including accuracy rate, accuracy rate, recall rate and F1 score, among which the accuracy is increased by 2 percentage points.