Tuning Windowed Chi-Squared Detectors for Sensor Attacks
For control systems security, this work provides a method to detect sensor attacks with dynamic detectors, offering a practical improvement over static approaches.
The paper proposes a model-based windowed chi-squared detector for identifying falsified sensor measurements, comparing it with static chi-squared and dynamic CUSUM detectors. It characterizes zero-alarm attacks and quantifies the advantage of dynamic detectors over static ones, with simulations on a chemical reactor showing improved detection performance.
A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to compare the performance of the windowed chi-squared detector. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors do not raise alarms (zero-alarm attacks). We quantify the advantage of using dynamic detectors (windowed chi-squared and CUSUM detectors), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations using a chemical reactor are presented to illustrate the performance of our tools.