Proactive Smart-Grid Defense: DRL-Powered Moving Target Defense Against False Data Injection Attacks

Cyber-Physical Power Systems (CPPS) combine physical processes with digital technologies to enhance power grid monitoring and control. Nonetheless, this integration introduced cybersecurity vulnerabilities that can bypass conventional Bad Data Detection (BDD) mechanisms and compromise state estimation accuracy. To counter these threats, Moving Target Defense (MTD) strategies employing Distributed Flexible AC Transmission System (D-FACTS) devices have been proposed, dynamically altering system parameters to invalidate the attacker’s knowledge of the system. While few approaches in the literature have been proposed to optimize perturbations introduced by the MTD mechanism, they often rely on computationally intensive iterative optimization techniques, which are challenging for real-time use. To address this gap, this paper proposes a novel approach to optimize the perturbation parameters of D-FACTS devices using Deep Reinforcement Learning (DRL), specifically leveraging the Deep Deterministic Policy Gradient (DDPG) and the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. The DRL-based method efficiently navigates the continuous action space of D-FACTS devices, enabling real-time adjustments that enhance the detection and identification of FDI attacks without the computational overhead of iterative methods. The proposed approach is validated on simulated IEEE 14-bus and IEEE 57-bus systems, which achieve up to 28% higher attack detection probability compared to Max-Rank MTD and a comparable performance to state-of-the-art iterative robust MTD, while providing a substantial reduction in computational time—up to several orders of magnitude—and maintaining high detection rates across varying attack intensities and perturbation configurations.