Detecting Cyber Attacks in Power System AGC Using a Drifted Ornstein-Uhlenbeck Process
Addresses the problem of detecting stealthy false data injection attacks in power system AGC for grid operators, offering a method that is independent of load observability and outperforms existing approaches.
Proposed a robust FDIA detection method for power system AGC using MLE of a drifted OU process, achieving accurate and rapid detection of sophisticated attacks, outperforming UIO (which missed detections) and LSTM-AE (which had prolonged detection times).
The Automatic Generation Control (AGC) system, reliant on real-time measurements over communication networks, is susceptible to stealthy false data injection attacks (FDIAs), risking equipment damage and economic losses. We propose a robust FDIA detection method using maximum likelihood estimation (MLE) of a drifted multivariate Ornstein-Uhlenbeck (OU) process. Independent of load observability, in various cyberattack scenarios, the proposed FDIA detection method delivers accurate and rapid detection of sophisticated FDIAs, outperforming traditional unknown input observer (UIO) methods, which miss detections, and Long Short-Term Memory Autoencoder (LSTM-AE) approaches, which suffer from prolonged detection times.