Neural Fictitious-Self Play-Based Cyber-Layer Defense for Frequency Control in Microgrids Against FDI Attacks

Securing secondary frequency control against increasing false data injection (FDI) attacks is crucial in microgrid systems. Although various detection systems (DSs) have been proposed for microgrids, false positives (FPs) and false negatives (FNs) in DSs introduce imperfect observations to the cyber defense system. Improper defense actions may reduce the system performance due to additional time delay and/or resource utilization. This paper designs a decentralized optimal decision-making scheme for cyber-layer defense to secure microgrid secondary frequency control against rational FDI attacks. Besides the capability of tackling imperfect observations from DSs, the proposed optimal defense decision-making scheme can maximize the long-term reward rather than a one-shot reward in response to FDI attacks. A multi-stage security game model is formulated, and cyber-physical states and controllability Gramians are jointly considered in the payoff function. The strategy realization-equivalent rule and Nash equilibrium (NE) are introduced to derive the optimal defense policy. A neural fictitious self-play (NFSP) is introduced to learn the optimal defense strategy. Simulation results show that the proposed method increases the successful defense ratio by 21.29% compared with the stochastic game solution when imperfect observations of DSs are considered.