Secure Fusion Estimation Against FDI Sensor Attacks in Cyber-Physical Systems

TL;DR

This paper develops a secure multi-sensor fusion estimation method for cyber-physical systems under false data injection attacks, utilizing local subsystem augmentation and Kalman filtering to detect and estimate attacks and system states.

Contribution

It introduces a novel augmentation-based fusion estimator that can simultaneously estimate system states and attack signals even when only some sensors are compromised.

Findings

The proposed method effectively detects FDI attacks.

It improves estimation accuracy under attack conditions.

Illustrative examples demonstrate its advantages.

Abstract

This paper is concerned with the problem of secure multi-sensors fusion estimation for cyber-physical systems, where sensor measurements may be tampered with by false data injection (FDI) attacks. In this work, it is considered that the adversary may not be able to attack all sensors. That is, several sensors remain not being attacked. In this case, new local reorganized subsystems including the FDI attack signals and un-attacked sensor measurements are constructed by the augmentation method. Then, a joint Kalman fusion estimator is designed under linear minimum variance sense to estimate the system state and FDI attack signals simultaneously. Finally, illustrative examples are employed to show the effectiveness and advantages of the proposed methods.