Detecting Stealthy False Data Injections on Cyber-Physical Systems using Temporal Distance Metrics

Adversarial methods like false data injection attacks (FDIAs) on sensors can manipulate system states in modern cyber-physical systems (CPSs). Such attacks may remain undetected by state-of-the-art statistical detectors, leading to an unsafe system state. In this work, we propose a novel detector neural network with the ability to differentiate between distributional shifts of sensor data under normal operation and adversarial attacks. This includes stealthy attacks that can bypass state-of-the-art detectors. The proposed scheme helps in securing resource-constrained safety-critical CPSs using lightweight solutions that employ compute and communication-heavy crypto methods for data exchange only upon detection of false data injection by an adversary.