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

2025-05-01

AI Summary

To address security vulnerabilities—particularly remote impersonation and proximity attacks—in vehicle-to-infrastructure (V2I) communications under non-line-of-sight (NLOS) wireless channels within intelligent transportation systems, this paper proposes the first multi-channel, multi-factor authentication framework integrating NLOS radio and line-of-sight (LOS) visible-light channels. A roadside unit issues a challenge, and the vehicle’s headlamp responds via coded optical flickering; a dual-channel CNN-LSTM model jointly decodes both temporal flash sequences and spatial features for closed-loop identity verification. Our key contributions include: (i) the first synergistic use of LOS visual and NLOS radio channels for V2I authentication; and (ii) a multi-condition robust training strategy accommodating variations in illumination, weather, vehicle speed, and inter-vehicle distance. Experimental evaluation achieves 95.0%–96.6% authentication accuracy; ablation studies and comparisons with state-of-the-art methods confirm substantial gains over single-channel baselines and demonstrate strong robustness in dynamic traffic scenarios.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Enhancing V2I communication security against impersonation attacks
• Combining NLOS and LOS channels for multi-factor authentication
• Improving authentication accuracy under diverse environmental conditions

Innovation

• Multi-Channel Multi-Factor Authentication for V2I
• Deep learning decodes headlight flashing sequences
• Dual-channel design enhances robust vehicle authentication