research-article

Detection and Avoidance of Wormhole Attacks in Connected Vehicles

Authors:

Sami S. Albouq,

Erik M. FredericksAuthors Info & Claims

DIVANet '17: Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

Pages 107 - 116

https://doi.org/10.1145/3132340.3132346

Published: 21 November 2017 Publication History

Abstract

The routing protocol in connected vehicles (CVs) is vulnerable to wormhole attacks where attackers can deceive legitimate nodes and purport them as if they are immediate or close neighbors. The Ad hoc On-Demand Distance Vector (AODV) protocol is a routing packet protocol designed for mobile nodes. However, AODV may not have been explicitly developed with security issues, specifically wormhole attacks, in mind, thereby requiring a detection algorithm to secure route establishment. This paper introduces the Wormhole-Protocol-Detector ( WPD ), a lightweight protocol for detecting and mitigating wormhole attacks. WPD is designed to work on a highway that is clustered into equal segments and equipped with road side units (RSUs) in predefined locations to monitor each segment. WPD consists of three phases: monitoring and detection of out-of-range packets, estimating the hop count between source and destination nodes, and identification of nodes participating in a wormhole connection. Together, these phases enable legitimate nodes to avoid the wormhole link and obtain secure routing paths between CVs. To validate our approach, we apply WPD to a CVs simulation where different types and lengths of wormhole, including a new wormhole attack method, can be applied to the CV network. Our experimental results suggest that WPD can detect wormhole attacks with a high detection rate and minimum false positives.

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Cited By

Jasti PGriffith HRathore H(2024)Detecting and Mitigating Colluding Attacks in Connected Vehicles using Reinforcement Learning2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444185(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444185
Vailoces GKeith AAlmehmadi AEl-Khatib KCoutinho RBani Younes M(2023)Securing the Electric Vehicle Charging Infrastructure: An In-Depth Analysis of Vulnerabilities and CountermeasuresProceedings of the Int'l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3616392.3623424(31-38)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616392.3623424
Sangwan ASangwan ASingh R(2022)A Classification of Misbehavior Detection Schemes for VANETs: A SurveyWireless Personal Communications10.1007/s11277-022-10098-1129:1(285-322)Online publication date: 30-Oct-2022
https://doi.org/10.1007/s11277-022-10098-1
Show More Cited By

Index Terms

Detection and Avoidance of Wormhole Attacks in Connected Vehicles
1. Security and privacy

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cover image ACM Conferences

DIVANet '17: Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

November 2017

160 pages

ISBN:9781450351645

DOI:10.1145/3132340

General Chair:
Mirela S.M.A. Notare
Faculdade de Tecnologia em Transporte Aéreo, Brazil
,
Program Chair:
Rodolfo Coutinho
UFMG, Brazil

Copyright © 2017 ACM.

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Published: 21 November 2017

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MSWiM '17: 20th ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

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Cited By

Jasti PGriffith HRathore H(2024)Detecting and Mitigating Colluding Attacks in Connected Vehicles using Reinforcement Learning2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444185(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444185
Vailoces GKeith AAlmehmadi AEl-Khatib KCoutinho RBani Younes M(2023)Securing the Electric Vehicle Charging Infrastructure: An In-Depth Analysis of Vulnerabilities and CountermeasuresProceedings of the Int'l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3616392.3623424(31-38)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616392.3623424
Sangwan ASangwan ASingh R(2022)A Classification of Misbehavior Detection Schemes for VANETs: A SurveyWireless Personal Communications10.1007/s11277-022-10098-1129:1(285-322)Online publication date: 30-Oct-2022
https://doi.org/10.1007/s11277-022-10098-1
Lamssaggad ABenamar NHafid AMsahli M(2021)A Survey on the Current Security Landscape of Intelligent Transportation SystemsIEEE Access10.1109/ACCESS.2021.30500389(9180-9208)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3050038
Sharma SKaushik B(2019)A survey on internet of vehicles: Applications, security issues & solutionsVehicular Communications10.1016/j.vehcom.2019.100182(100182)Online publication date: Oct-2019
https://doi.org/10.1016/j.vehcom.2019.100182
Singh PGupta RNandi SNandi S(2019)Machine Learning Based Approach to Detect Wormhole Attack in VANETsPrimate Life Histories, Sex Roles, and Adaptability10.1007/978-3-030-15035-8_63(651-661)Online publication date: 15-Mar-2019
https://doi.org/10.1007/978-3-030-15035-8_63
Zhang DYu FYang RTang HNotare MSun PCoutinho R(2018)A Deep Reinforcement Learning-based Trust Management Scheme for Software-defined Vehicular NetworksProceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3272036.3272037(1-7)Online publication date: 25-Oct-2018
https://dl.acm.org/doi/10.1145/3272036.3272037

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