Detection of Data Injection Attacks on Decentralized Statistical Estimation

Or Shalom, Amir Leshem, Anna Scaglione, Angelia Nedic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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 the attackers' main goal is to steer the network's final state to a state of their choice. We show that the detection metric of the straightforward attack scheme proposed by Wu et. at in [1], is vulnerable to a more sophisticated attack. To overcome this attack we propose a novel metric that can be computed locally by each agent to detect the presence of an attacker in the network, as well as a metric that localizes the attackers in the network. We conclude the paper with simulations supporting our findings.

Original languageEnglish
Title of host publication2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663783
DOIs
StatePublished - 2 Jul 2018
Event2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/TerritoryIsrael
CityEilat
Period12/12/1814/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Convex optimization
  • Data injection attacks
  • Decentralized optimization
  • Distributed projected gradient
  • Maximum likelihood

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