Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data

TL;DR

This paper analyzes the asymptotic performance of distributed Bayesian detection systems under Byzantine data falsification attacks, deriving conditions under which the attack can blind the fusion center and optimal attack strategies.

Contribution

It provides a theoretical framework for understanding the impact of Byzantines on detection performance and derives closed-form expressions for attack strategies and the minimum power needed to blind the system.

Findings

Above a certain Byzantine fraction, detection becomes impossible.

Closed-form expressions for optimal Byzantine attack strategies.

Derived the minimum attack power to blind the fusion center.

Abstract

In this letter, we consider the problem of distributed Bayesian detection in the presence of data falsifying Byzantines in the network. The problem of distributed detection is formulated as a binary hypothesis test at the fusion center (FC) based on 1-bit data sent by the sensors. Adopting Chernoff information as our performance metric, we study the detection performance of the system under Byzantine attack in the asymptotic regime. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.