# Tuning Windowed Chi-Squared Detectors for Sensor Attacks

arXiv: https://arxiv.org/abs/1710.02573 · HTML: https://mlsanity.com/paper/1710.02573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Learning-Based Vulnerability Analysis of Cyber-Physical Systems](https://mlsanity.com/paper/2103.06271/) (2021)
- [Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning](https://mlsanity.com/paper/2603.27592/) (2026)

## Abstract (arXiv)

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.