Despite the broad applications of Vehicular Ad hoc Networks (VANETs) enabling communication among vehicles and roadside infrastructure to enhance traffic efficiency and road safety, they are susceptible to various security threats. A Denial of Service (DoS) attack is one such threat that poses a significant challenge to the participating entities in the vehicular network, which prevents them from accessing the resources and services as legitimate network participants. In this paper, we propose a lightweight hybrid approach that combines deep and machine learning (ML) models capable of performing feature engineering to preserve the privacy of the data communicated. It lowers the complexity and eliminates unused communication information for misbehaviour detection. Our proposed DoS attack detection technique aims to perform effectively with a limited storage capacity of onboard vehicle units, making it suitable for real-world deployment for misbehaviour detection.


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    Title :

    Lightweight Hybrid Approach for DoS Attack Detection in VANET


    Contributors:


    Publication date :

    2024-11-17


    Size :

    489950 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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