Localization of Data Injection Attacks on Distributed M-Estimation

Or Shalom, Amir Leshem, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which attackers control legitimate nodes in the network and use them to inject false data. We have previously shown [1] that the detection metric by Wu et. al in [2], is vulnerable to sophisticated attacks where the attacker mixes normal behaviour and false data injection. In this paper we propose a novel metric that can be computed locally by each agent to detect and localize the novel attack in the network in a single instance.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728107080
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings


Conference2019 IEEE Data Science Workshop, DSW 2019
Country/TerritoryUnited States


  • Convex optimization
  • Data injection attacks
  • Decentralized optimization
  • Distributed projected gradient
  • M-Estimators

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality


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