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.
Group Secret Key Generation for Vehicular Networks Based on Physical Layer Security
12.05.2025
409201 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Group Secret Key Generation Using Physical Layer Security for UAV Swarm Communications
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