A Multi-Observer Approach for Attack Detection and Isolation of Discrete-Time Nonlinear Systems
This addresses security vulnerabilities in control systems for applications like industrial automation, but it is incremental as it builds on existing observer-based methods.
The paper tackles the problem of detecting and isolating sensor attacks in discrete-time nonlinear systems under false data injection and noise, using a bank of Input-to-State Stable observers to check consistency with attack-free trajectories, with performance demonstrated through simulations.
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.