Detection and Prevention of DoS Attack in VANET Using Artificial Neural Network

In the realm of Vehicular Ad Hoc Networks (VANETs), seamless communication between vehicles is crucial for the exchange of essential messages, such as road traffic updates and accident-related information. The dynamic nature of vehicular movement demands timely information dissemination to ensure effective traffic management and safety. However, the continuous availability of the network is imperative for the smooth functioning of VANETs. The vulnerability of VANETs to Denial-of-Service (DoS) attacks poses a significant threat, potentially rendering the network unavailable. Such attacks can disrupt communication among vehicles, leading to adverse consequences. This paper presents an enhanced approach for detecting DoS attacks, aiming to identify and mitigate them promptly. Leveraging neural networks, our proposed technique surpasses the efficacy of previous methods, ensuring improved results in the timely detection of DoS attacks within VANETs.