This paper introduces an innovative misbehavior detection (MBD) algorithm designed to identify false sudden stop information within Cooperative Intelligent Transportation Systems (C-ITS) using Collective Perception Messages (CPMs). The algorithm integrates a vehicle perception model and CPM data from neighboring vehicles. Through comprehensive simulations conducted with Omnet++, SUMO, and Veins, we explore the algorithm’s performance across various perception ranges. Additionally, we investigate the impact of potential attacks on the system, analyzing changes in average speed and average delay in travel time. Our results demonstrate the algorithm’s high efficacy in detecting false stop information and offer valuable insights into the relationship between perception range and detection performance. The proposed approach contributes to enhancing the security and safety of C-ITS.


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    Titel :

    Distributed Misbehavior Detection based on Vehicle Perception Model and CPM Data Collection


    Beteiligte:


    Erscheinungsdatum :

    2023-10-10


    Format / Umfang :

    1077884 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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