Localization of Data Injection Attacks on Distributed M-Estimation

Or Shalom; Amir Leshem; Anna Scaglione

doi:10.1109/DSW.2019.8755572

Localization of Data Injection Attacks on Distributed M-Estimation

Or Shalom, Amir Leshem, Anna Scaglione

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

3 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 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 language	English
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	22-26
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755572
State	Published - Jun 2019
Externally published	Yes
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: 2 Jun 2019 → 5 Jun 2019

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	2/06/19 → 5/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

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

Access to Document

10.1109/DSW.2019.8755572

Cite this

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title = "Localization of Data Injection Attacks on Distributed M-Estimation",

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.",

keywords = "Convex optimization, Data injection attacks, Decentralized optimization, Distributed projected gradient, M-Estimators",

author = "Or Shalom and Amir Leshem and Anna Scaglione",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

year = "2019",

month = jun,

doi = "10.1109/DSW.2019.8755572",

language = "אנגלית",

series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

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}

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., 8755572, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 22-26, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 2/06/19. https://doi.org/10.1109/DSW.2019.8755572

Localization of Data Injection Attacks on Distributed M-Estimation. / Shalom, Or; Leshem, Amir; Scaglione, Anna.
2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 22-26 8755572 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

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

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AB - 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.

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Localization of Data Injection Attacks on Distributed M-Estimation

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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