Online Cyber-Attack Detection in the Industrial Control System: A Deep Reinforcement Learning Approach

Abstract

In the open network environment, industrial control systems face huge security risks and are often subject to network attacks. The existing abnormal detection methods of industrial control networks have the problem of a low intelligence degree of adaptive detection and recognition. To overcome this problem, this article makes full use of the advantages of deep reinforcement learning in decision-making and builds a learning system with continuous learning ability. Specifically, industrial control network and deep reinforcement learning characteristics are applied to design a unique reward and learning mechanism. Moreover, an industrial control anomaly detection system based on deep reinforcement learning is constructed. Finally, we verify the algorithm on the gas pipeline industrial control dataset of Mississippi State University. The experimental results show that the convergence rate of this model is significantly higher than that of traditional deep learning methods. More importantly, this model can get a higher F1 score.