Poster: A Machine Learning based Hybrid Trust Management Heuristic for Vehicular Ad hoc Networks

Abstract

Over the past few decades, Vehicular Ad hoc Networks have attracted the attention of numerous researchers from both academia and industry. Today, this promising wireless communication technology plays an indispensable role as vehicles exchange low-latent safety critical messages with one another in a bid to make the road traffic more safer, efficient, and convenient. However, dissemination of malicious messages within the network not only significantly reduces the network performance but also becomes a source of threat for the passengers and vulnerable pedestrians. Accordingly, a number of trust models have been recently proposed in the literature to ensure the identification and elimination of malicious vehicles from the network. These trust models primarily rely on the aggregation of both direct and indirect observations and evict the malicious vehicles based on a particular threshold set on this composite trust value. Nevertheless, setting-up of this threshold poses a significant challenge especially owing to diverse influential factors in such a dynamic and distributed networking environment. To this end, in this manuscript, machine learning has been employed to compute the aggregate trust score for flagging and evicting of the malicious vehicles from a vehicular network. It is evident from the simulated results that the devised method is both accurate and scalable.