An energy efficient access control for secured intelligent transportation system for 6G networking in VANET

Abstract

The Intelligent Transport System (ITS) is very prominent due to its connection with the Internet of Things (IoT), which enhances passenger security and comfort. The Vehicular Ad-hoc Network (VANET) is a component of ITS. It manages the techniques used for routing and privacy in autonomous cars. The increasing number of autonomous cars has exceeded the capacity of current wireless networks for transmission. It is expected that the proposed 6G wireless network can meet VANET criteria. Very little research has investigated the privacy concerns of VANETs in 6G networking connections. This work presents a method for dealing with authentic and privacy concerns for automobiles in VANETs. Our solution strengthens the vehicle's connectivity system by detecting malicious attacks like replay attacks, DoS attacks, and impersonification attacks. The proposed system uses batch authentication to reduce traffic and workload on the network. The proposed system employs both ID-based authentication and deep learning methods. Where the role of ID-based authentication is to check for access in the network, deep learning takes on the role of identifying all the malicious packets in the system. Our result also shows that the proposed system can identify malicious packets with an accuracy of 98.25% and works successfully in 6G networking communication.