Every year, the automobile sector experiences significant technology breakthroughs, which drive innovations in connected and autonomous cars. However, these developments increase cybersecurity concerns such as information theft, eavesdropping, and impersonation attacks, which compromise the integrity and safety of vehicle networks. In this paper, we provide a group secret key (GSK) generation technique designed specifically for vehicular networks. Our technique uses physical layer security to provide a shared GSK for all vehicles in a specific fleet, allowing for safe mutual authentication. The GSK is generated using the Doppler shift values and the channel randomness between pairs of vehicles, therefore using the inherent dynamics and communication patterns of vehicular movement. In addition, we ran extensive machine learning experiments to determine the accuracy and reliability of estimating the generated common key. The experimental findings show that machine learning models can predict the GSK, lowering the Key Error Rate (KER) and improving the overall security and reliability of group authentication in automotive networks.


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    Title :

    Group Secret Key Generation for Vehicular Networks Based on Physical Layer Security


    Contributors:


    Publication date :

    2025-05-12


    Size :

    409201 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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