Resilient distributed estimation against FDI attacks: A correntropy-based approach

Abstract

In this paper, we consider the resilient distributed estimation in adversarial networks, compromised by false data injection (FDI) attacks. We develop a two-step distributed approach to detect the compromised nodes of the network due to FDI attacks. Specifically, we develop a distributed correntropy-based discrimination scheme followed by a distributed state perception scheme to distinguish the compromised nodes from the secure, such that the reliable neighbors could be determined by each node in a distributed manner. We further theoretically analyze the mean and mean-square performance of the resilient distributed estimation, and propose an optimal reference neighbor selection scheme to enhance the network estimation performance. Illustrative simulations validate the superior performance of both FDI attack detection and parameter estimation of the proposed distributed algorithms for adversarial networks, especially under heavy and time-varying FDI attacks. The theoretical results well predict the performance of the proposed resilient distributed estimation algorithm.