A Multi-Observer Approach for Attack Detection and Isolation of Discrete-Time Nonlinear Systems

TL;DR

This paper proposes a multi-observer framework for detecting and isolating sensor attacks in discrete-time nonlinear systems, leveraging ISS properties to ensure robustness against false data injections and measurement noise.

Contribution

It introduces two novel algorithms that utilize a bank of ISS observers to detect and isolate sensor attacks in nonlinear systems, enhancing robustness and reliability.

Findings

Algorithms effectively detect sensor attacks in simulations.

ISS-based approach improves attack isolation accuracy.

Robustness demonstrated under measurement noise conditions.

Abstract

We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are `consistent' with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms.