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

TL;DR

This paper introduces a novel multi-channel, multi-factor authentication scheme for V2I communication that combines cryptographic credentials with visual signals and deep learning decoding, significantly enhancing security and reliability.

Contribution

The paper presents a unified MFA scheme using visual challenge-response and deep learning decoding, improving security against impersonation and proximity attacks in V2I systems.

Findings

Achieved over 95% accuracy in real-world tests under various conditions.

Demonstrated the effectiveness of dual-channel deep learning models for decoding and authentication.

Validated robustness of the approach across different lighting, weather, and speed scenarios.

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.