Detection of data injection attacks in decentralized learning

Reinhard Gentz, Hoi To Wai, Anna Scaglione, Amir Leshem

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

5 Scopus citations

Abstract

Gossip based optimization and learning are appealing methods that solve big data learning problems sharing computation and network resources when data are distributed. The main advantage these methods offer is that they are fault tolerant. Their flat architecture, however, expands the attack surface in the case of a data injection attack. We analyze the effects of data injection on the asymptotic behavior of the network and draw a parallel with the case of opinion dynamics in a network where zealots inject opinions to mislead a community. We further propose a possible decentralized detection of such attacks and analyze its performance.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages350-354
Number of pages5
Volume2016-February
ISBN (Print)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

Keywords

  • attack detection
  • data injection attack
  • decentralized learning
  • randomized gossip protocol

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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    Gentz, R., Wai, H. T., Scaglione, A., & Leshem, A. (2016). Detection of data injection attacks in decentralized learning. In Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 2016-February, pp. 350-354). [7421145] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421145