## 1 Introduction
Multiple wireless channels are imported into the VANET that demonstrate the network architecture, make the network more secure and not vulnerable to attacks, and provide the latest coverage area to design and develop for wireless communication technologies [7]. Real-time malicious nodes are a critical concern in vehicular ad hoc networks (VANET) because they can cause severe disruption and pose a significant threat to the security and privacy of communication. VANETs rely on communication between vehicles and infrastructure to ensure smooth and safe transportation. Malicious nodes can exploit these communication channels to launch attacks on the network and cause widespread damage [8]. The importance of real-time malicious nodes in VANETs lies in their ability to compromise the network's security and privacy. Malicious nodes can exploit network vulnerabilities and steal sensitive information, such as vehicle location, speed, and driving habits. They can also send false messages, which can cause confusion, accidents, and even gridlock on the roads. Moreover, real-time malicious nodes can launch denial of service (DoS) attacks, which can significantly disrupt the network's operation [9]. DoS attacks can cause communication breakdowns, leading to delays in emergency services, traffic congestion, and accidents. Therefore, it is essential to detect and isolate real-time malicious nodes in VANETs to ensure the network's security and reliability. VANETs need advanced security protocols to detect and prevent attacks from malicious nodes. Security measures such as encryption, authentication, and intrusion detection systems are necessary to mitigate the impact of real-time malicious nodes in VANETs [10]. In DDoS, intruders use multiple vehicles to target the network from different locations, disturbing the network density and integrity. However, all of these vehicles involved in DDoS attacks are unaware they are utilizing the attack on the network devices. All of these vehicles are also called \"zombies\" in the DDoS context. The results of DDoS attacks can be sewers, such as loss of human lives, accidents to automatic vehicles, and infrastructure loss. As multiple zombies are involved in this attack, detection is difficult, and there is no proper conveyancing [11]. Figure 1 shows the attack on vehicles using DDoS. In this DDoS attack from Figure 1, the attackers use other vehicles to attack single data or a major vehicle under this attack. The targeted vehicles stop working and leave for another environment. The victim vehicle did not correspond to these changes and started to drop out of the responding phase in the network. In these theses, we focused on the real-time detection and mitigation of DDoS attacks using machine learning. VANET is one of the wireless multi-hop network (WMN) cases. This network provides fast technology change in different infrastructure management and enhances the work of different resources under a high level of mobility [13]. In VANET, the devices are equipped with wireless communication devices and computing technologies for inter-vehicle and intra-vehicle communication. Inter-vehicle communication, in the form of caching, is one of the promising fields of research in these development perspectives for standardization, research, and development technologies [14]. \"VANETs enable a wide range of applications, such as prevention of collisions, safety, blind crossing, dynamic route scheduling, real-time traffic condition monitoring, etc. Another important application for VANETs is providing Internet connectivity to vehicular nodes. Figure 2 shows an example of a VANET\". From Figure 2, we adopted the communication using 802.11 for the V2V, V2I, and I2I communication architecture. The vehicles, RSU, and based stations use the communication architecture to explain the current scenario [15]. Figure 1: DDoS Attack on vehicles in the VANET environment [12]. VANET provides three main components, which include the application unit (AU), on-board unit (OBU), and roadside unit (RSU) [17].