Distributed Bayesian Detection in the Presence of Byzantine Data

Bhavya Kailkhura, Yunghsiang S. Han, Swastik Brahma, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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.

Original languageEnglish (US)
Article number7134807
Pages (from-to)5250-5263
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume63
Issue number19
DOIs
StatePublished - Oct 1 2015

Keywords

  • Bayesian detection
  • Byzantine data
  • data falsification
  • distributed detection
  • probability of error

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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