Distributed inference with M-Ary quantized data in the presence of byzantine attacks

V. Sriram Siddhardh Nadendla, Yunghsiang S. Han, Pramod K. Varshney

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

The problem of distributed inference with M-ary quantized data at the sensors is investigated in the presence of Byzantine attacks. We assume that the Byzantine nodes attack the inference network by modifying the symbol corresponding to the quantized data to one of the other symbols in the quantization alphabet-set and transmitting falsified symbol to the fusion center (FC). In this paper, we find the optimal Byzantine attack that blinds any distributed inference network. As the quantization alphabet size increases, a tremendous improvement in the security performance of the distributed inference network is observed. In addition to the perfect channel case, in Appendix A, we also analyze the optimal Byzantine attack when the channel between the nodes and the FC is noisy and is modelled as a discrete M-ary channel. We also investigate the optimal attack within the restricted space of highly-symmetric attack strategies, that maximally degrades the performance of the inference network in the presence of resource-constrained Byzantine attacks. A reputation-based scheme for identifying malicious nodes is also presented as the network's strategy to mitigate the impact of Byzantine threats on the inference performance of the distributed sensor network. We also provide asymptotic analysis to find the optimal reputation-based scheme as a function of the fraction of compromised nodes in the network.

Original languageEnglish (US)
Article number6778791
Pages (from-to)2681-2695
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume62
Issue number10
DOIs
StatePublished - May 15 2014

Keywords

  • Byzantine attacks
  • Distributed inference
  • fisher information
  • kullback-leibler divergence
  • network-security
  • sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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