Adaptive window-based sensor attack detection for cyber-physical systems

Abstract

Sensor attacks alter sensor readings and spoof Cyber-Physical Systems (CPS) to perform dangerous actions. Existing detection works tend to minimize the detection delay and false alarms at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should dynamically balance the two metrics when a physical system is at different states. Along with this argument, we propose an adaptive sensor attack detection system that consists of three components - an adaptive detector, detection deadline estimator, and data logger. It can adapt the detection delay and thus false alarms at run time to meet a varying detection deadline and improve usability (or false alarms). Finally, we implement our detection system and validate it using multiple CPS simulators and a reduced-scale autonomous vehicle testbed.