###### Abstract  
A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters. artificial neural network; clustering; AODV; VANET; secure routing; black hole attack +
Footnote †: journal:  
including a reference to a previous contribution [1]. Subsequently, an extensive review of the latest literature published in esteemed research journals is undertaken. The proposed protocol and its associated algorithms and methodology are presented in the following section. Results are presented, highlighting a state-of-the-art comparison with existing techniques, thereby underscoring the superior performance of the proposed approach in contrast to recent literature. The research concludes with a summary of findings and outlines future directions [2]. Everyday human life involves travel, with vehicles constituting an indispensable mode of transportation. VANETs form a network of nodes, primarily consisting of vehicles, interconnected through a wireless topology. Communication within this network is facilitated by RSUs [3]. Information and communication technologies (ICT) are instrumental in the intelligent transportation system (ITS), promoting safer, more efficient, thoughtful, and straightforward communication and information sharing. VANET employs wireless networks to enable efficient and remote communication among vehicles providing transportation services. However, addressing various research challenges in vehicular ad hoc networks remains formidable [4]. Firms adhering to IEEE 14712000 [5] and ISO/IEC 42010 [6] architectural standards play a crucial role in establishing VANET technology. The VANET system comprises three domains: mobile, web, and social media [7]. Within these domains, two categories, infrastructure and generic, are distinguished. The mobile domain encompasses vehicle and mobile device domains. In contrast, the infrastructure domain consists of management centers, such as traffic and vehicle management centers, and roadside devices, like traffic lights [8]. VANET systems exhibit variations based on location [9].