Vehicular networks are expected to be the key technologies in the age of intelligent transportation and connected intelligence. However, by broadcasting false maneuver information in vehicular networks (emergency brake, merging/changing lane), an attacker can cause many vehicles to be disoriented or even crash in severe accidents. This work introduces a robust misbehavior detection scheme, namely TRIMO, by exploiting multimodal learning from various independent data sources (e.g., camera, joint radar and communications in the sixth-generation (6G) mobile networks). TRIMO can determine whether a car is lying about its sharing data with up to 92.7 percent accuracy by examining the consistency of data from numerous sources.


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    Titel :

    TRIMO: An Efficient Multimodal Misbehavior Detection Model in Vehicular Networks


    Beteiligte:
    Nguyen, Van-Linh (Autor:in) / Nguyen, Lan-Huong (Autor:in) / Liu, Wen-Pin (Autor:in) / Ting, Hao-En (Autor:in) / Hu, Xuan-Zhang (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    3633760 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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