Distributed Multiple Attacks Detection via Consensus AA-GMPHD Filter
Chaoqun Yang, Xianghui Cao, Lidong He, Heng Zhang
Abstract
This article is concerned with the problem of multiple attacks detection (MAD) for distributed sensor networks (SNs) under multiple malicious attacks. The goal of this article is to develop an effective method capable of simultaneously detecting multiple attacks in distributed SNs. By integrating the theories of random finite set (RFS), fusion rules, and consensus, a novel distributed filter named consensus arithmetic average Gaussian mixture probability hypothesis density (AA-GMPHD) filter is proposed in this article, which can achieve the simultaneous detection of multiple attacks in the context of distributed SNs. The main contribution of this article, lies in the proposed consensus AA-GMPHD filter that solves the MAD problem in distributed SNs for the first time. Simulation experiments confirm the effectiveness of the proposed filter for the distributed MAD problem in the context of distributed SNs.