Empirical investigation of VANET-based security models from a statistical perspective

Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.