Support vector machine (SVM) based sybil attack detection in vehicular networks

Pengwenlong Gu; Rida Khatoun; Youcef Begriche; Ahmed Serhrouchni

doi:10.1109/WCNC.2017.7925783

Support vector machine (SVM) based sybil attack detection in vehicular networks

Pengwenlong Gu
, Rida Khatoun
, Youcef Begriche
, Ahmed Serhrouchni

Université Paris-Saclay

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

Abstract

Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.

Original language	English
Title of host publication	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509041831
DOIs	https://doi.org/10.1109/WCNC.2017.7925783
Publication status	Published - 10 May 2017
Externally published	Yes
Event	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States Duration: 19 Mar 2017 → 22 Mar 2017

Publication series

Name	IEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)	1525-3511

Conference

Conference	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Country/Territory	United States
City	San Francisco
Period	19/03/17 → 22/03/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Intrusion detection
Machine learning
Sybil attack
Vehicle driving pattern
Vehicular networking

Access to Document

10.1109/WCNC.2017.7925783

Cite this

Gu, P., Khatoun, R., Begriche, Y., & Serhrouchni, A. (2017). Support vector machine (SVM) based sybil attack detection in vehicular networks. In 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings Article 7925783 (IEEE Wireless Communications and Networking Conference, WCNC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC.2017.7925783

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title = "Support vector machine (SVM) based sybil attack detection in vehicular networks",

abstract = "Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.",

keywords = "Intrusion detection, Machine learning, Sybil attack, Vehicle driving pattern, Vehicular networking",

author = "Pengwenlong Gu and Rida Khatoun and Youcef Begriche and Ahmed Serhrouchni",

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year = "2017",

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doi = "10.1109/WCNC.2017.7925783",

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series = "IEEE Wireless Communications and Networking Conference, WCNC",

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Gu, P, Khatoun, R, Begriche, Y & Serhrouchni, A 2017, Support vector machine (SVM) based sybil attack detection in vehicular networks. in 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings., 7925783, IEEE Wireless Communications and Networking Conference, WCNC, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017, San Francisco, United States, 19/03/17. https://doi.org/10.1109/WCNC.2017.7925783

Support vector machine (SVM) based sybil attack detection in vehicular networks. / Gu, Pengwenlong; Khatoun, Rida; Begriche, Youcef et al.
2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7925783 (IEEE Wireless Communications and Networking Conference, WCNC).

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

TY - GEN

T1 - Support vector machine (SVM) based sybil attack detection in vehicular networks

AU - Gu, Pengwenlong

AU - Khatoun, Rida

AU - Begriche, Youcef

AU - Serhrouchni, Ahmed

PY - 2017/5/10

Y1 - 2017/5/10

N2 - Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.

AB - Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.

KW - Intrusion detection

KW - Machine learning

KW - Sybil attack

KW - Vehicle driving pattern

KW - Vehicular networking

UR - https://www.scopus.com/pages/publications/85019755746

U2 - 10.1109/WCNC.2017.7925783

DO - 10.1109/WCNC.2017.7925783

M3 - Conference contribution

AN - SCOPUS:85019755746

T3 - IEEE Wireless Communications and Networking Conference, WCNC

BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017

Y2 - 19 March 2017 through 22 March 2017

ER -

Gu P, Khatoun R, Begriche Y, Serhrouchni A. Support vector machine (SVM) based sybil attack detection in vehicular networks. In 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7925783. (IEEE Wireless Communications and Networking Conference, WCNC). doi: 10.1109/WCNC.2017.7925783

Support vector machine (SVM) based sybil attack detection in vehicular networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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