# Secure State Estimation against Sensor Attacks in the Presence of Noise

arXiv: https://arxiv.org/abs/1510.02462 · HTML: https://mlsanity.com/paper/1510.02462/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy](https://mlsanity.com/paper/2108.11769/) (2021)
- [Online Learning with Adversaries: A Differential-Inclusion Analysis](https://mlsanity.com/paper/2304.01525/) (2023)
- [Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System](https://mlsanity.com/paper/2305.00982/) (2023)

## Abstract (arXiv)

We consider the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm, and derive (optimal) bounds on the achievable state estimation error given an upper bound on the number of attacked sensors. The proposed state estimator involves Kalman filters operating over subsets of sensors to search for a sensor subset which is reliable for state estimation. To further improve the subset search time, we propose Satisfiability Modulo Theory based techniques to exploit the combinatorial nature of searching over sensor subsets. Finally, as a result of independent interest, we give a coding theoretic view of attack detection and state estimation against sensor attacks in a noiseless dynamical system.