## 1 Introduction  
The automobile industry is becoming more inclined toward launching smart vehicles integrated with AI, automation, and the Internet of Vehicles. Thus, these new waves of technological development would promote safety and traffic management and also upgrade the general practical experience of vehicle operation in a significant way. But all these prove troublesome from a security perspective as connectivity increases the threat of cyber attacks, with the greatest threats being in the form of a Distributed Denial of Service (DDoS) attack [1, 2]. Although the Internet of Vehicles (IoVs) also offers an interactive environment to vehicles, humans, and roadside infrastructures, serious cybersecurity drawbacks come with these improvements. One DDoS threat, a form of cyberspace war, is vertically oriented and centralized. Typical communication modes include vehicle-to-vehicle (V2V) for facilitating cars while they communicate with other cars through communication infrastructure, or the Internet. This is also known as the Internet of Vehicles (IoV). Although the Internet of Vehicles (IoVs) improves mobility by integrating communication between vehicles, people, and infrastructure, many security aspects must be addressed. One of the most serious non-malware threats is Distributed Denial of Service (DDoS) attacks as they can bring down communication networks and create danger to public safety. These attacks specifically target key connections such as the following: Vehicle to Infrastructure (V2I), which refers to equipment like traffic lights and toll booths, which, in turn, facilitate the interconnection between vehicles and road infrastructure; Vehicle to Pedestrian (V2P), which is designed to make it safe for interaction between vehicles and pedestrians; Vehicle to Cloud (V2C), where vehicles are connected to the cloud for data processing and vital updates; and Vehicle to Sensor (V2S), [3] where vehicles use various sensors as supplements to their core functionality, including the use of sensor fusion as depicted in Figure 1. In this study, we focus on the detection of DDoS attacks in the IoV ecosystem, which target the IP and UDP protocols. These types of attacks occur frequently in IoV systems because they have the potential capability for overloading the communication channel in vehicle-to-vehicle and vehicle-to-infrastructure networks. In flooding these channels with a large number of packets, the real-time data exchanges may be disrupted due to IP/UDP-based DDoS attacks, resulting in deteriorating network performance and vehicle safety. The integration of artificial intelligence and IoV technologies has enabled vehicles to operate autonomously, diagnose issues in real-time, and communicate with infrastructure. Self-driving abilities allow these vehicles to achieve some of these tasks with minimal assistance from human beings [4], which are features that improve usability among transport consumers. In addition, the IoV realizes the interconnectivity of vehicles with other subsystems, including traffic signals and road sensors, which form this complex transportation system. This network enhances effective communication and data sharing in real-time, hence increasing efficiency in traffic control and reducing traffic accidents [5]. At the same  
Figure 1: Internet of Vehicles (IoV) illustrating various communication types. time, the very connectivity that offers these opportunities attracts new threats as well. Such threats, in this instance, are the DDoS attacks. In a DDoS attack, a group of systems are used to engage a target network with more traffic than it can handle. In the case of smart vehicles and IoV systems, this implies that it is easy for attackers to interrupt and delay the typical operation of vehicle communication networks [6]. A prominent example of such a large-scale DDoS-based attack is the 2016 Dyn cyberattack, which targeted a major DNS provider and resulted in widespread outages across key services.