Research on interacted response technology of cyber security protection devices based on deep reinforcement learning oriented to new generation of power system
Yaofu Cao, Xiaomeng Li, Junwen Liu, Junlu Yan, Jingcheng Zhao, Huimin Li · 2022
The existing power system cyber security protection system lacks pertinence for new services and has a low degree of automation. It is urgent to propose a security protection framework for the characteristics of new generation of power system business, especially to improve the attack prevention capability of distributed power sources, flexible and adjustable loads, etc., ensuring the stable operation of the power grid. Aiming at the lack of efficient linkage and coordination of existing cyber security protection devices in new generation of power systems, this paper proposes an interacted response technology for security protection devices based on intelligent learning. Our scheme standardizes and correlates the various threat intelligence collected by the security protection equipment, and use the multi-agent deep reinforcement learning method to realize the issuance and optimization of automated strategies in combination with the analysis results. The interacted strategy of safety protection devices is optimized through collaborative heterogeneous reinforcement learning model. And the validity of interacted configuration is verified for correctness, completeness, redundancy and consistency.