Data injection attack on decentralized optimization

Sissi Xiaoxiao Wu; Hoi To Wai; Anna Scaglione; Angelia Nedic; Amira Leshem

doi:10.1109/icassp.2018.8462528

Data injection attack on decentralized optimization

Sissi Xiaoxiao Wu, Hoi To Wai, Anna Scaglione, Angelia Nedic, Amira Leshem

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

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

15 Scopus citations

Abstract

This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedić et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3644-3648
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/icassp.2018.8462528
State	Published - 10 Sep 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

This work is supported by the National Natural Science Foundation of China under Grant 009989, the US National Science Foundation EAGER CCF 1553746, NSF CCF-BSF 1714672, and BSF Grant 2016660.

Funders	Funder number
US National Science Foundation	CCF 1553746
National Science Foundation	2016660, CCF-BSF 1714672
National Natural Science Foundation of China	009989

Keywords

Data injection attack
Decentralized optimization
Gossip algorithms

Access to Document

10.1109/icassp.2018.8462528

Cite this

Xiaoxiao Wu, S., Wai, H. T., Scaglione, A., Nedic, A., & Leshem, A. (2018). Data injection attack on decentralized optimization. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 3644-3648). Article 8462528 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icassp.2018.8462528

Xiaoxiao Wu, Sissi ; Wai, Hoi To ; Scaglione, Anna et al. / Data injection attack on decentralized optimization. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3644-3648 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Data injection attack on decentralized optimization",

abstract = "This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedi{\'c} et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.",

keywords = "Data injection attack, Decentralized optimization, Gossip algorithms",

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Xiaoxiao Wu, S, Wai, HT, Scaglione, A, Nedic, A & Leshem, A 2018, Data injection attack on decentralized optimization. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8462528, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 3644-3648, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/icassp.2018.8462528

Data injection attack on decentralized optimization. / Xiaoxiao Wu, Sissi; Wai, Hoi To; Scaglione, Anna et al.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3644-3648 8462528 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

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

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N2 - This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedić et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.

AB - This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedić et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.

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Xiaoxiao Wu S, Wai HT, Scaglione A, Nedic A, Leshem A. Data injection attack on decentralized optimization. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3644-3648. 8462528. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/icassp.2018.8462528

Data injection attack on decentralized optimization

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Keywords

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