Vehicular Ad-hoc Networks (VANETs) serve as the basis of Intelligent Transportation Systems (ITS) enhancing the safety on roads and driving conditions. A rogue node in a VANET may launch Sybil attacks by broadcasting mischievous information to make an impression of false road congestion. Sybil attacks can be the cause for rerouting of vehicles, traffic congestion, collision of vehicles, and be an opening for launching other attacks. Prior research used different approaches such as cryptography, trust scores, past vehicle data, blockchain, machine learning, etc. to detect Sybil attacks. However, the false-positive rate (FPR), processing delay, and overhead of these techniques are high. In this paper, we present a technique for detecting Sybil attacks in VANETs using fog computing and beamforming, called FSDV-H (Fog computing-based Sybil attack Detection in VANETs for Highway scenarios). The onboard units of vehicles in a specific area are used in FSDV-H to create a dynamic fog for spotting potential Sybil attacks based on vehicle speed before validating the suspicions with a beamforming-based location verification. The goals of FSDV-H are to reduce FPR, data processing delay, and overhead at high vehicular density in a highway scenario. Performance evaluations using SUMO and OMNET++ simulators confirm that FSDV-H offers 69% lower FPR, 36% lower processing delay, and 7% lower overhead at high vehicular densities in a highway scenario in comparison to existing Sybil attack detection techniques.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sybil Attack Detection in VANETs using Fog Computing and Beamforming


    Contributors:


    Publication date :

    2023-10-12


    Size :

    1917137 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Enhancing Security in VANETs with Sybil Attack Detection using Fog Computing

    Paranjothi, Anirudh / Khan, Mohammad S. | IEEE | 2023


    Fast tracing method for Sybil attack in VANETs

    Zhang, Zhaoyi / Lai, Yingxu / Chen, Ye et al. | IEEE | 2023


    Study of the Impact of Sybil Attack in VANETs Using F2MD

    Pavithra, T. / Nagabhushana, B. S. / Das, Suchismitha | Springer Verlag | 2023


    Cross-layer scheme for detecting large-scale colluding Sybil attack in VANETs

    Rabieh, Khaled / Mahmoud, Mohamed M. E. A. / Guo, Terry N. et al. | IEEE | 2015


    Dealing with Sybil Attack in VANET

    Pattanayak, Binod Kumar / Pattnaik, Omkar / Pani, Sasmita | Springer Verlag | 2020