Securing Cyber-Physical Systems Against GPS Spoofing Attacks Using Confidence Attribution

This work addresses the use of a machine-learning based confidence attribution scheme to detect GPS spoofing attacks against cyber-physical systems. The confidence attribution scheme assigns a continuous value, between 0% and 100%, to each measurement, expressing how consistent this measurement is with measurements from other sensors in a cyber-physical system. By verifying the consistency of GPS measurements when compared to other sensors available, such as those present in an inertial measurement unit, it is possible to detect GPS spoofing attacks. The solution is evaluated on an experimentally acquired dataset containing flight logs, including sensor measurements and control signals, of an UAV subject to GPS spoofing attacks. The results show the number of anomalies detected by the confidence attribution scheme increases significantly in the presence of a GPS spoofing attack, rendering its detection.