Vehicular Ad-Hoc Networks (VANETs) have become an integral component of contemporary vehicular technology. This technology provides advanced features like traffic and weather reports and collision prevention to create a sophisticated driving experience. Unfortunately, this infrastructure can become victim to cyber-attacks like intrusions that aim to compromise VANET operations. In this paper, we propose a novel lightweight machine learning (ML)-based intrusion detection framework for VANETs. The algorithm developed utilizes dimensionality reduction to reduce the operational footprint of our training data so that it is easier to train and deploy ML-enabled intrusion detectors for protecting VANET infrastructures. Results showcase that $K=3$ provides us with high performance and low operational requirements. Through this, we achieve efficient performance for protecting VANETs against intrusions while decreasing the overall operational footprint of the framework.


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    Titel :

    Small, but Mighty: Lightweight ML-Enabled Intrusion Detection Framework for Vehicular Ad-Hoc Networks


    Beteiligte:
    Dinh, Minh (Autor:in) / Patel, Megha (Autor:in) / Das, Tapadhir (Autor:in) / Shukla, Raj Mani (Autor:in)


    Erscheinungsdatum :

    27.07.2024


    Format / Umfang :

    429113 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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