Gaussian Filtering with Cyber-Attacked Data

Abstract

Gaussian filtering is a commonly used nonlinear filtering method. This letter proposes an advanced Gaussian filtering method for handling cyber-attacked measurement data. It considers three general forms of measurement data irregularities due to the attack, including false data injection (FDI), time asynchronous measurements (TAM), and denial-of-service (DoS). The proposed method introduces a modified measurement model to incorporate the possibility of these irregularities occurring simultaneously. Subsequently, it re-derives the traditional Gaussian filtering for the modified measurement model, resulting in the proposed filtering method. The improved accuracy of the proposed method is validated for two simulation problems. © 1994-2012 IEEE.