Localization of Data Injection Attacks on Distributed M-Estimation

Or Shalom, Amir Leshem, Anna Scaglione

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

Abstract

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.
Pages22-26
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 1 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

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

Keywords

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

ASJC Scopus subject areas

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

Cite this

Shalom, O., Leshem, A., & Scaglione, A. (2019). Localization of Data Injection Attacks on Distributed M-Estimation. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 22-26). [8755572] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755572