Tuning Windowed Chi-Squared Detectors for Sensor Attacks

TL;DR

This paper introduces a model-based windowed chi-squared detector for identifying falsified sensor data, comparing its performance with static and dynamic detectors through simulations on a chemical reactor.

Contribution

It develops a windowed chi-squared detection method and characterizes attack-induced state degradation while comparing dynamic and static detectors.

Findings

Dynamic detectors outperform static ones in leveraging state history.

Windowed chi-squared effectively detects sensor falsification.

Simulations demonstrate improved detection performance.

Abstract

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.