Graph Autoencoder-Based Detection of Unseen False Data Injection Attacks in Smart Grids

A major concern in smart power grids is when malicious or manipulated data is injected into measurement data due to malicious activities. Several approaches have been investigated to counter such false data injection attacks (FDIAs). However, such data-driven detectors present two major limitations. First, they neglect capturing the grid’s spatial characteristics. Second, they offer limited attack identification to familiar types of FDIAs since they are present within the model’s train sets. To conquer such limitations, we propose the use of an artificial intelligence-based graph autoencoder (GAE) for FDIAs detection. Our proposed detector offers three main advantages compared to existing detectors. First, it employs the operation of graph convolution to apprehend the grid’s spatial characteristics. Second, it offers an unsupervised autoencoder-based anomaly detection that requires only benign samples under normal operation for training. Third, it outperforms existing detectors by 16–47% in FDIAs detection rate (DR) when tested against unseen FDIAs on an IEEE 39-bus system.