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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.

The Automatic Generation Control (AGC), a fundamental frequency control system, is vulnerable to cyber-physical attacks. Coordinated false data injection attack, aiming to generate fake transient measurements, typically precedes unwarranted actions, inducing frequency excursion, leading to electromechanical swings between generators, blackouts, and costly equipment damage. Unlike other works that focus on point anomaly detection, this work focuses on contextual detection of stealthy cyber-attacks against AGC by utilizing prior information, which is essential for power system operation and situational awareness. More specifically, we depart from the traditional deep learning anomaly detection that is thoroughly driven by black-box detection; instead, we envision an approach based on physics-informed hybrid deep learning detection 'CLDPhy,’ which utilizes the combination of prior knowledge of physics and system metrics. Our method, to the extent of our knowledge, is the first context-based anomaly detection for stealthy cyber-physical attacks against the AGC system. We evaluate our approach on an industrial high-class PowerWorld simulated dataset - based on the IEEE 37-bus model. Our experiments observe a 36.4% improvement in accuracy for coordinated attack detection with contextual information, and our approach clearly demonstrates the superiority in comparison with other baselines.