Detecting and Localizing Adversarial Nodes Usig Neural Networks

Gangqiang Li; Sissi Xiaoxiao Wu; Shengli Zhang; Hoi To Wai; Anna Scaglione

doi:10.1109/SPAWC.2018.8445849

Detecting and Localizing Adversarial Nodes Usig Neural Networks

Gangqiang Li, Sissi Xiaoxiao Wu, Shengli Zhang, Hoi To Wai, Anna Scaglione

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

3 Scopus citations

Abstract

This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.

Original language	English (US)
Title of host publication	2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538635124
DOIs	https://doi.org/10.1109/SPAWC.2018.8445849
State	Published - Aug 24 2018
Event	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece Duration: Jun 25 2018 → Jun 28 2018

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2018-June

Other

Other	19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Country/Territory	Greece
City	Kalamata
Period	6/25/18 → 6/28/18

Keywords

Gossip algorithm
average consensus
insider attacks
neural networks

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Access to Document

10.1109/SPAWC.2018.8445849

Cite this

Li, G., Xiaoxiao Wu, S., Zhang, S., Wai, H. T., & Scaglione, A. (2018). Detecting and Localizing Adversarial Nodes Usig Neural Networks. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 Article 8445849 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2018.8445849

Detecting and Localizing Adversarial Nodes Usig Neural Networks. / Li, Gangqiang; Xiaoxiao Wu, Sissi; Zhang, Shengli et al.
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8445849 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2018-June).

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

Li, G, Xiaoxiao Wu, S, Zhang, S, Wai, HT & Scaglione, A 2018, Detecting and Localizing Adversarial Nodes Usig Neural Networks. in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018., 8445849, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, Kalamata, Greece, 6/25/18. https://doi.org/10.1109/SPAWC.2018.8445849

Li G, Xiaoxiao Wu S, Zhang S, Wai HT, Scaglione A. Detecting and Localizing Adversarial Nodes Usig Neural Networks. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8445849. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/SPAWC.2018.8445849

Li, Gangqiang ; Xiaoxiao Wu, Sissi ; Zhang, Shengli et al. / Detecting and Localizing Adversarial Nodes Usig Neural Networks. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

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title = "Detecting and Localizing Adversarial Nodes Usig Neural Networks",

abstract = "This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.",

keywords = "Gossip algorithm, average consensus, insider attacks, neural networks",

author = "Gangqiang Li and {Xiaoxiao Wu}, Sissi and Shengli Zhang and Wai, {Hoi To} and Anna Scaglione",

note = "Funding Information: This work is supported by the National Natural Science Foundation of China under Grant 61701315, the US National Science Foundation EAGER CCF 1553746, NSF CCF-BSF 1714672, and BSF Grant 2016660. Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

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doi = "10.1109/SPAWC.2018.8445849",

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N1 - Funding Information: This work is supported by the National Natural Science Foundation of China under Grant 61701315, the US National Science Foundation EAGER CCF 1553746, NSF CCF-BSF 1714672, and BSF Grant 2016660. Publisher Copyright: © 2018 IEEE.

PY - 2018/8/24

Y1 - 2018/8/24

N2 - This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.

AB - This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.

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Detecting and Localizing Adversarial Nodes Usig Neural Networks

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