Detection of data injection attacks in decentralized learning

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

doi:10.1109/ACSSC.2015.7421145

Detection of data injection attacks in decentralized learning

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 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 language	English
Title of host publication	Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	350-354
Number of pages	5
ISBN (Electronic)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421145
State	Published - 26 Feb 2016
Externally published	Yes
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: 8 Nov 2015 → 11 Nov 2015

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2016-February
ISSN (Print)	1058-6393

Conference

Conference	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	8/11/15 → 11/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

attack detection
data injection attack
decentralized learning
randomized gossip protocol

Access to Document

10.1109/ACSSC.2015.7421145

Cite this

Gentz, R., Wai, H. T., Scaglione, A., & Leshem, A. (2016). Detection of data injection attacks in decentralized learning. In M. B. Matthews (Ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 (pp. 350-354). Article 7421145 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421145

@inproceedings{69dd3517b4f14fe39a8a64f9281bbdd2,

title = "Detection of data injection attacks in decentralized learning",

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.",

keywords = "attack detection, data injection attack, decentralized learning, randomized gossip protocol",

author = "Reinhard Gentz and Wai, {Hoi To} and Anna Scaglione and Amir Leshem",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 08-11-2015 Through 11-11-2015",

year = "2016",

month = feb,

day = "26",

doi = "10.1109/ACSSC.2015.7421145",

language = "אנגלית",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "350--354",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015",

address = "ארצות הברית",

}

Gentz, R, Wai, HT, Scaglione, A & Leshem, A 2016, Detection of data injection attacks in decentralized learning. in MB Matthews (ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015., 7421145, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2016-February, IEEE Computer Society, pp. 350-354, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 8/11/15. https://doi.org/10.1109/ACSSC.2015.7421145

Detection of data injection attacks in decentralized learning. / Gentz, Reinhard; Wai, Hoi To; Scaglione, Anna et al.
Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. ed. / Michael B. Matthews. IEEE Computer Society, 2016. p. 350-354 7421145 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of data injection attacks in decentralized learning

AU - Gentz, Reinhard

AU - Wai, Hoi To

AU - Scaglione, Anna

AU - Leshem, Amir

PY - 2016/2/26

Y1 - 2016/2/26

N2 - 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.

AB - 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.

KW - attack detection

KW - data injection attack

KW - decentralized learning

KW - randomized gossip protocol

UR - http://www.scopus.com/inward/record.url?scp=84969836578&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2015.7421145

DO - 10.1109/ACSSC.2015.7421145

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84969836578

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 350

EP - 354

BT - Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015

Y2 - 8 November 2015 through 11 November 2015

ER -

Gentz R, Wai HT, Scaglione A, Leshem A. Detection of data injection attacks in decentralized learning. In Matthews MB, editor, Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. IEEE Computer Society. 2016. p. 350-354. 7421145. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2015.7421145

Detection of data injection attacks in decentralized learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this