Vehicular Communication Security: Multi-Channel and Multi-Factor Authentication

• URL: http://arxiv.org/abs/2505.00340v1
• Date: Thu, 01 May 2025 06:36:24 GMT
• Title: Vehicular Communication Security: Multi-Channel and Multi-Factor Authentication
• Authors: Marco De Vincenzi, Shuyang Sun, Chen Bo Calvin Zhang, Manuel Garcia, Shaozu Ding, Chiara Bodei, Ilaria Matteucci, Dajiang Suo,
• Abstract summary: Vehicle-to-Infrastructure (V2I) communication plays a key role in enabling mobility-enhancing and safety-critical services.<n>Current V2I authentication relies on credential-based methods over wireless Non-Line-of-Sight (NLOS) channels.<n>We propose a unified Multi-Channel, Multi-Factor Authentication scheme that combines NLOS cryptographic credentials with a Line-of-Sight (LOS) visual channel.
• Score: 7.883758003805773
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Secure and reliable communications are crucial for Intelligent Transportation Systems (ITSs), where Vehicle-to-Infrastructure (V2I) communication plays a key role in enabling mobility-enhancing and safety-critical services. Current V2I authentication relies on credential-based methods over wireless Non-Line-of-Sight (NLOS) channels, leaving them exposed to remote impersonation and proximity attacks. To mitigate these risks, we propose a unified Multi-Channel, Multi-Factor Authentication (MFA) scheme that combines NLOS cryptographic credentials with a Line-of-Sight (LOS) visual channel. Our approach leverages a challenge-response security paradigm: the infrastructure issues challenges and the vehicle's headlights respond by flashing a structured sequence containing encoded security data. Deep learning models on the infrastructure side then decode the embedded information to authenticate the vehicle. Real-world experimental evaluations demonstrate high test accuracy, reaching an average of 95% and 96.6%, respectively, under various lighting, weather, speed, and distance conditions. Additionally, we conducted extensive experiments on three state-of-the-art deep learning models, including detailed ablation studies for decoding the flashing sequence. Our results indicate that the optimal architecture employs a dual-channel design, enabling simultaneous decoding of the flashing sequence and extraction of vehicle spatial and locational features for robust authentication.

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