首页|Reinforcement Learning-empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-physical Systems
Reinforcement Learning-empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-physical Systems
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国家科技期刊平台
NETL
NSTL
万方数据
The massive integration of communication and in-formation technology with the large-scale power grid has en-hanced the efficiency,safety,and economical operation of cyber-physical systems.However,the open and diversified communica-tion environment of the smart grid is exposed to cyber-attacks.Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid.Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types.To address the issue,we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network(GCN)framework to detect data integrity attacks in cyber-physical systems.The simulation results show that the proposed framework is scalable and achieves higher detection accuracy,unlike other benchmark techniques.
Deep reinforcement learninggraph convolutional networkheterogeneous smart grid network
国家科技期刊平台
NSTL
万方数据
