Enhancing 5G-VANET Environments with SDN-Based Package Filtering for Improved Networking

Abstract

This research explores the integration of Software-Defined Networking into 5G-enabled Vehicular Ad Hoc Networks (5G-VANETs) with a focus on the implementation of a package filtering model. The goal is to enhance networking within these dynamic environments. By leveraging SDN's centralized control capabilities, this study aims to optimize network performance, enhance security, and improve the quality of service in 5G-VANETs. The outcomes of this research have the potential to contribute significantly to the advancement of intelligent transportation systems and vehicular communication networks. As the VANET milieu operates dynamically, the major issues are to afford high reliability, low latency, and high bandwidth. 5G significantly provides seamless connectivity and ultrafast speed for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Advancements in internet of vehicle brought few security issues such as illegal device access, data injection, and man-in-the-middle attacks. To avoid these issues, this work implements the Package Filtering Model (PFM) that is trained using machine learning and ready to use for real-time detection. It filters out the fraudulent transaction based on the frequency and behavioural characteristics of the communication. The simulation results ensure that the proposed mechanism affords enhanced packet delivery, lower transmission delay, minimum fraud package, and bare minimum block processing time compared to the existing state-of-the-art mechanisms.