Distributed Bayesian Detection with Byzantine Data

TL;DR

This paper investigates how Byzantine nodes in a distributed Bayesian detection network can disrupt the fusion center's ability to accurately detect a hypothesis, deriving conditions and strategies for effective attacks and their impact.

Contribution

It introduces a comprehensive analysis of Byzantine attacks in distributed detection, including minimum attack power, optimal attack strategies, and limitations of existing asymptotic results.

Findings

Above a certain Byzantine fraction, detection becomes impossible.

Derived closed-form optimal attack strategies for Byzantines.

Existing asymptotic results do not hold in non-asymptotic scenarios.

Abstract

In this paper, we consider the problem of distributed Bayesian detection in the presence of Byzantines in the network. It is assumed that a fraction of the nodes in the network are compromised and reprogrammed by an adversary to transmit false information to the fusion center (FC) to degrade detection performance. The problem of distributed detection is formulated as a binary hypothesis test at the FC based on 1-bit data sent by the sensors. 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. We analyze the problem under different attacking scenarios and derive results for different non-asymptotic cases. It is found that existing asymptotics-based results do not hold under several non-asymptotic scenarios. 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.