Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection (2411.12130v1)

Abstract: Smart inverters are instrumental in the integration of renewable and distributed energy resources (DERs) into the electric grid. Such inverters rely on communication layers for continuous control and monitoring, potentially exposing them to cyber-physical attacks such as false data injection attacks (FDIAs). We propose to construct a defense strategy against a priori unknown FDIAs with a multi-agent reinforcement learning (MARL) framework. The first agent is an adversary that simulates and discovers various FDIA strategies, while the second agent is a defender in charge of detecting and localizing FDIAs. This approach enables the defender to be trained against new FDIAs continuously generated by the adversary. The numerical results demonstrate that the proposed MARL defender outperforms a supervised offline defender. Additionally, we show that the detection skills of an MARL defender can be combined with that of an offline defender through a transfer learning approach.