Adversarial Gaussian Process Regression in Sensor Networks

Cyber‐physical systems are fundamental to operations of many safety critical systems, from power plants to autonomous cars. Such systems feature a control loop that maps sensor measurements to control decisions. In many applications, these decisions involve maintaining system state features, such as temperature and pressure, in a safe range, with anomaly detection employed to ensure that anomalous or malicious sensor measurements do not subvert system operation. Although anomaly detection has been studied in the literature, many existing approaches focus on the cases with passive adversaries. Our first contribution is a novel stealthy attack on systems featuring Gaussian Process regression (GPR) for anomaly detection—a popular choice for this task. Next, we pose the problem of robust GPR for anomaly detection as a Stackelberg game and present a novel algorithmic approach for solving it. Our experimental evaluation demonstrates both the vulnerability of baseline systems to attack, as well as the increased robustness offered by our approach.