Detection and mitigation of biasing attacks on distributed estimation networks

Mohammad Deghat, Valery Ugrinovskii, Iman Shames, Cédric Langbort

Research output: Contribution to journalArticle

Abstract

The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizing the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.

Original languageEnglish (US)
Pages (from-to)369-381
Number of pages13
JournalAutomatica
Volume99
DOIs
StatePublished - Jan 2019

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Keywords

  • Consensus
  • Distributed attack detection
  • Large-scale systems
  • Vector dissipativity

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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