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Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency. [ABSTRACT FROM AUTHOR]

Information is the driving force in vehicular ad hoc networks (VANET) since vehicles share information (emergency, general, and multimedia). VANET communicates between vehicles using a unique routing protocol, unlike other wireless routing technologies. Many protocols, techniques, and approaches have been developed to secure and protect data. To enhance current security and privacy measures and develop and model new ones, the ideas of machine learning (ML), deep learning (DL), and artificial intelligence are being applied. In this paper, we provide information on the various types of attacks that target VANET communication, VANET layers, the security goals that are affected, and real-time attacks that occur on manufacturing hubs. We compared various VANET attack prevention, detection, and AI techniques proposed, as well as future research work in the field of VANET, for improving accuracy, security, and privacy. [ABSTRACT FROM AUTHOR]