Further results on noise-enhanced distributed inference in the presence of Byzantines

Aditya Vempaty, V. Sriram Siddhardh Nadendla, Pramod K. Varshney

Research output: Contribution to journalConference Articlepeer-review

3 Scopus citations


The problem of Byzantine (malicious sensors) threats in a distributed detection framework for inference networks is addressed. Impact of Byzantines is mitigated by suitably adding Stochastic Resonance (SR) noise. Previously, Independent Malicious Byzantine Attack (IMBA), where each Byzantine decides to attack the network independently relying on its own observation was considered. In this paper, we present further results for Cooperative Malicious Byzantine Attack (CMBA), where Byzantines collaborate to make the decision and use this information for the attack. In order to analyze the network performance, we consider KL-Divergence (KLD) to quantify detection performance and minimum fraction of Byzantines needed to blind the network (αblind) as a security metric. We show that both KLD and αblind increase when SR noise is added at the honest sensors. When SR noise is added to the fusion center, we analytically show that there is no gain in terms of αblind or the network-wide performance measured in terms of the deflection coefficient. We also model a game between the network and the Byzantines and present a necessary condition for a strategy (SR noise) to be a saddle-point equilibrium.

Original languageEnglish (US)
Article number6618550
JournalInternational Symposium on Wireless Personal Multimedia Communications, WPMC
StatePublished - 2013
Event2013 16th International Symposium on Wireless Personal Multimedia Communications, WPMC 2013 - Co-located with Global Wireless Summit 2013 - Atlantic City, NJ, United States
Duration: Jun 24 2013Jun 27 2013


  • Byzantine Attack
  • Inference Networks
  • Noise-Enhanced Signal Processing
  • Stochastic Resonance
  • Zero-Sum Game

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction


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