Enhancing Security and Efficiency in Vehicle-to-Sensor Authentication: A Multi-Factor Approach with Cloud Assistance
Xinrui Zhang, Pincan Zhao, Jason Jaskolka · 2024
Connected and Autonomous Vehicles (CAVs) can improve their perception by integrating data from roadside sensors. However, ensuring secure authentication between CAVs and sensors is challenging due to the limited capabilities of sensors and the growing number of vehicles. This paper introduces a secure authentication protocol that enables direct communication between CAVs and roadside sensors, addressing a critical gap in existing research focused on vehicle-to-cloud authentication. The proposed multi-factor authentication scheme combines password, biometric, and device-specific factors with Elliptic Curve Cryptography (ECC) and efficient key agreement protocols. A comprehensive adversary model tailored for vehicular networks is presented, along with an in-depth security analysis demonstrating the scheme’s resilience against various threats. The cloud-assisted authentication framework offloads computationally intensive tasks to the cloud server, reducing the burden on resource-constrained Roadside Units (RSUs) and ensuring scalability. Extensive performance evaluations showcase the scheme’s computational efficiency, low communication overhead, and storage costs compared to state-of-the-art solutions, highlighting its practical feasibility and potential for real-world deployment in intelligent transportation systems.