Privacy-Aware and Efficient Model for Secure Infrastructure in Software-Defined Vehicular Networks

Abstract

The rapid advancement of software-defined vehicular networks (SDVN) has transformed transportation systems by introducing programmability, flexibility, and centralized management. By decoupling the control and data planes, SDVN enhances network efficiency and adaptability, thereby enabling real-time traffic management and intelligent decision-making. However, this centralization also presents significant security and privacy risks, exposing networks to threats such as unauthorized access, data breaches, and malware infections. To address these challenges, we propose a secure and privacy-respecting infrastructure for SDVN, integrating advanced cryptographic techniques and lightweight authentication mechanisms. Our model utilizes the Edwards-curve digital signature algorithm (EdDSA) for authentication, elliptic curve Diffie-Hellman (ECDH) for key exchange, and an enhanced certificate revocation list (CRL) to strengthen security. This approach aims to provide low-latency authentication, robust data protection, and improved privacy preservation, while ensuring efficient resource utilization in SDVN. Through verification and analysis, including simulation comparisons showing 20% improvement in authentication time and 15% reduced computation overhead, we demonstrate the effectiveness of our model in securing vehicular communications against emerging cyber threats.