Artificial neural network-based secured communication strategy for vehicular ad hoc network

Vehicular ad hoc network (VANET) is an application-based network belonging to the class of mobile ad hoc networks. The nodes in the VANET are interconnected and communicate through wireless media and the Internet. This subsequently leads to data security issues. Several secured communication and routing protocols for VANET have been proposed so far. However, security complaints are still rising. The motivation is to provide a secured communication strategy (SCS) in which each node of VANET needs to be authenticated and authorized to participate in the communication network. The SCS comprises two phases, namely node authentication and authorization phases. The details of every node are collected and saved in the authentication phase and validated in the authorization phase. Whenever a new node is admitted into the VANET functionalities, it must fulfill the requirement of answering a set of credentials given by the admin. The logistic regression model is used to overcome the computation complexity. The simulation of SCS is carried out in NS2 software, and the results are verified in terms of throughput, packet delivery ratio, packet loss, and delay. The performance is evaluated by comparing the results with earlier methods to prove its efficiency.