Robust Data-Driven Adversarial False Data Injection Attack Detection Method With Deep Q-Network in Power Systems

Xiaohong Ran, Wee Peng Tay, Christopher H. T. Lee

Abstract: Electric power systems have been increasingly subjected to false data injection attacks (FDIAs) and adversarial examples, which inject well-designed disturbance signals into the measurements, and thereby generate erroneous state estimation (SE) results. The present work addresses this issue by proposing a robust data-driven attack detection algorithm. We apply a novel metric denoted as Euclidian distance similarity ratio for detecting stealthy attack during the SE process. Second, two different deep Q networks are, respectively, employed for detecting FDIAs and adversarial examples based on their respective inflection points (IPs). We also propose sufficient and necessary conditions for the successful detection of adversarial examples based on the corresponding analyses of IPs. Finally, two networks are trained using deep reinforcement learning. The effectiveness of the proposed robust detection method is demonstrated based on simulations involving IEEE 14, 57, and 118 bus power systems.