Deep Reinforcement Learning Based Secure Communication and Computing Resource Allocation for Grid Cyber-Physical System

Grid Cyber-Physical System (CPS) improves the intelligence of the grid by combining computing, communication and control technologies, but this new grid CPS system may also have some new security risks, such as new types of attacks on the connection between the physical and information networks. In this paper, we propose a deep reinforcement learning-based joint optimization scheme to improve the security and resource efficiency of multiple grid sensors by exploiting physical layer security (PLS) techniques in a scenario where a malicious eavesdropper can wiretap confidential grid information. We use Wyner’s wiretap coding scheme to prevent confidential information from being decoded and eavesdropped by malicious eavesdroppers. We minimize the system processing latency while securing the wireless communication process by jointly optimizing the transmit power of the grid sensor and the allocation of computing resource blocks. The optimization problem in this paper is formulated as a multi-agent cooperative optimal decision problem and is solved using a double deep Q-network algorithm. Simulation results demonstrate the robustness and effectiveness of the scheme in ensuring information security and reducing delay.