A Privacy-Preserving Attack-Resistant Trust Model for Internet of Vehicles Ad Hoc Networks

Junejo, Muhammad Haleem, Ab Rahman, Ab Al Hadi, Shaikh, Riaz Ahmed, Mohamad Yusof, Kamaludin, Memon, Imran, Fazal, Hadiqua and Kumar, Dileep (2020) A Privacy-Preserving Attack-Resistant Trust Model for Internet of Vehicles Ad Hoc Networks. Scientific Programming, 2020. ISSN 1058-9244

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Abstract

The Internet of things (IoT) and advancements of wireless technology have evolved intelligent transport systems to integrate billion of smart objects ready to connect to the Internet. The modern era of the Internet of things (IoT) has brought significant development in vehicular ad hoc networks (VANETs) which transformed the conventional VANET into the Internet of Vehicle (IoV) to improve road safety and diminished road congestion. However, security threats are increasing due to dependency on infrastructure, computing, dynamic nature, and control technologies of VANET. The security threats of VANETs could be addressed comprehensively by increasing trustworthiness on the message received and transmitting node. Conversely, the presence of dishonest vehicles, for instance, Man in the Middle (MiTM) attackers, in the network sharing malicious content could be posed as a severe threat to VANET. Thus, increasing trustworthiness among nodes can lead to increased authenticity, privacy, accuracy, security, and trusted information sharing in the VANET. In this paper, a lightweight trust model is proposed, presented model identifying dishonest nodes and revoking its credential in the MiTM attack scenario. Furthermore, addressing the privacy and security requirement, the pseudonym scheme is used. All nodes in the VANET established trust provided by initially RSU, which is a trusted source in the network. Extensive experiments are conducted based on a variety of network scenarios to evaluate the accuracy and performance of the presented lightweight trust model. In terms of recall, precision, and F-score, our presented model significantly outperformed compared to MARINE. The simulation results have validated that the proposed lightweight model realized a high trust level with 40% of MiTM attackers and in terms of F-score 95%, whereas the MARINE model has 90%, which leads to the model to attain high detection accuracy.

Item Type: Article
Additional Information: Publisher Copyright: © 2020 Muhammad Haleem Junejo et al.
Uncontrolled Keywords: software,computer science applications,sdg 14 - life below water ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 16 Aug 2022 15:31
Last Modified: 24 Sep 2022 07:03
URI: https://ueaeprints.uea.ac.uk/id/eprint/87322
DOI: 10.1155/2020/8831611

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