Machine Learning based Trust Model for Secure Internet of Vehicle Data Exchange

Internet of vehicles (IoV) is an eminent road-side communication assistive technology that is designed to meet the service requirements of the users. As autonomous vehicles are the base for IoV environment, selecting authenticated neighbours is a prime task for improving the security of the communication environment. In this article, classification based trust model (CTM) for IoV is designed for improving the detection and verification of data and vehicles in the dynamic scenario. The classification and verification follows naïve Bayes machine learning model for distinguishing the vehicles as trusted and untrusted. Depending on the communication attributes of the vehicles, classification distribution is modelled for direct trust in both independent and joint manner. The proposed model is assessed using simulation experiments and it is seen that this model achieves fair trusted and untrusted vehicle detection and less verification delay.