Poster: Physics-Informed Augmentation for Contextual Anomaly Detection in Smart Grid

Abstract

Smart Grid (SG) networks, as a part of critical national infrastructure, are vulnerable to sophisticated cyber-physical attacks. Specifically, a coordinated false data injection attack aiming to generate fake transient measurements in the SG's Automatic Generation Control (AGC), can cause unwarranted actions and blackouts in the worst scenario. Unlike other works that overlook contextual correlations, this work utilizes contextual prior information and a temporal model to detect cyber-attacks. Specifically, we depart from the traditional deep learning anomaly detection, driven by black-box detection; instead, we envision an approach based on physics-informed hybrid deep learning detection. Our approach utilizes the combination of process control-based variational autoencoder, prior knowledge of physics, and long short-term memory for a false data injection attack detection. To the best of our knowledge, our method is the first contextual-based anomaly detection that incorporates process control-based prior information in the smart grid. The proposed approach is evaluated on the modified high-class PowerWorld simulated dataset based on the IEEE 37-bus model. Our experiments observe the lowest reconstruction error and offer 96.9% accuracy, demonstrating superiority over other baselines.