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

Marco De Vincenzi, Shuyang Sun, Chen Bo Calvin Zhang, Manuel Garcia, Shaozu Ding, Chiara Bodei, Ilaria Matteucci, Sanjay E. Sarma, Dajiang Suo

TL;DR

This paper tackles the vulnerability of V2I authentication to remote impersonation and proximity attacks by proposing a unified Multi-Channel MFA that fuses NLOS cryptographic credentials with a LOS visual channel. The method implements a challenge-response where the infrastructure issues a challenge and the vehicle responds by flashing its headlights, with the response decoded by a SlowFast CNN on the RSU, enabling near real-time verification. Key contributions include a novel LOS-based authentication mechanism, hardware-in-the-loop and real-vehicle validation, and detailed ablations that show the dual-channel design and SlowFast architecture yield high accuracy (≈95–97%) under varied conditions. The approach promises practical, low-cost deployment by leveraging existing headlight infrastructure, standard OCC, and PKI/TLS-based cryptography, potentially increasing security for ITS services without imposing significant computational overhead.

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.

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

TL;DR

This paper tackles the vulnerability of V2I authentication to remote impersonation and proximity attacks by proposing a unified Multi-Channel MFA that fuses NLOS cryptographic credentials with a LOS visual channel. The method implements a challenge-response where the infrastructure issues a challenge and the vehicle responds by flashing its headlights, with the response decoded by a SlowFast CNN on the RSU, enabling near real-time verification. Key contributions include a novel LOS-based authentication mechanism, hardware-in-the-loop and real-vehicle validation, and detailed ablations that show the dual-channel design and SlowFast architecture yield high accuracy (≈95–97%) under varied conditions. The approach promises practical, low-cost deployment by leveraging existing headlight infrastructure, standard OCC, and PKI/TLS-based cryptography, potentially increasing security for ITS services without imposing significant computational overhead.

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.
Paper Structure (27 sections, 15 figures, 3 tables)

This paper contains 27 sections, 15 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Motivations
  3. Our contribution
  4. Organization of the Paper
  5. Related work
  6. Attack Model
  7. Multi-Channel Multi-Factor Scheme
  8. Challenge-Response mechanism
  9. The Optical Camera Communication channel
  10. Security Analysis
  11. Implementation setup
  12. Testbeds setup
  13. RC-car testbed
  14. Real-car testbed
  15. Datasets Creation
  16. ...and 12 more sections

Figures (15)

  • Figure 1: Critical ITS scenarios requiring authentication mechanisms, with attacker devices and channels controlled by attackers highlighted in red.
  • Figure 2: Two-channels (NLOS and LOS) authentication process.
  • Figure 3: The 14-bit security frame and its components.
  • Figure 4: Implementation of our vehicle setup with RC-car and real-car, showcasing different headlight configurations.
  • Figure 5: The Phase 2 road set up: RSU simulation with camera and edge computer.
  • ...and 10 more figures