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Network Attack Path Selection and Evaluation Based on Q-Learning

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
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Appl. Sci. 2021, 11(1), 285; https://doi.org/10.3390/app11010285
Received: 23 October 2020 / Revised: 20 December 2020 / Accepted: 26 December 2020 / Published: 30 December 2020
As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method. View Full-Text
Keywords: power CPS; data tampering attack; risk assessment; Q-Learning algorithm; Fuzzy Petri Net power CPS; data tampering attack; risk assessment; Q-Learning algorithm; Fuzzy Petri Net
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MDPI and ACS Style

Wu, R.; Gong, J.; Tong, W.; Fan, B. Network Attack Path Selection and Evaluation Based on Q-Learning. Appl. Sci. 2021, 11, 285. https://doi.org/10.3390/app11010285

AMA Style

Wu R, Gong J, Tong W, Fan B. Network Attack Path Selection and Evaluation Based on Q-Learning. Applied Sciences. 2021; 11(1):285. https://doi.org/10.3390/app11010285

Chicago/Turabian Style

Wu, Runze, Jinxin Gong, Weiyue Tong, and Bing Fan. 2021. "Network Attack Path Selection and Evaluation Based on Q-Learning" Applied Sciences 11, no. 1: 285. https://doi.org/10.3390/app11010285

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