# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 1 Introduction  
In recent years, the vehicular ad-hoc network (VANET) has gained prominence in the networking world. It is a mobile ad hoc network (MANET) primarily utilized for vehicle-to-vehicle and vehicle-to-roadside-unit communication. Data is shared via vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communications. VANET applies to various areas such as traffic control, traffic congestion monitoring, and driver safety programs [1; 2]. However, VANET has its disadvantages, such as in dynamic behavior. This issue is rectified by an effective and efficient routing protocol that broadcasts the data. As cars may not be able to communicate information without an efficient routing method, they will lose all the benefits of sophisticated VANET technology [3]. Recently, many efforts have been made to adapt software-defined network (SDN) ideas to vehicular networks to benefit from the SDN's flexibility and programmability. The ultimate objective of this adaption is to improve VANET performance and make it more suitable for certain services and applications [4]. The software-defined network (SDN) is one of the emerging revolutionary network concepts utilized in data centers and operator networks to satisfy the growing demands of future networks [5]. Network programmability, centralized management control, and interface openness are some features of SDN. It also substantially simplifies network administration and improves networkability [6; 7]. SDN is divided into three layers: data, control, and application. Routers, switches, vehicles, and wireless gadgets are all part of the data layer. According to the controller, which would be the SDN's brain and resides in the control plane, devices in the data layer are responsible for packet transmission. It determines how data layer devices send and receive data and how the flow is managed. The network layer consists of software initiatives that manage the network's programming. SDN and VANET techniques are not without their flaws. Despite their numerous advantages and excellent performance, they are subject to attack risk, namely from DoS and DDoS, just as in traditional wireless networks. However, SDN features make it simpler to identify and respond to DoS attacks, and the openness of the data plane creates favorable conditions for additional DoS attacks [8]. A distributed DoS (DDoS) assault occurs when a DoS attack is launched from several vehicles dispersed throughout the network. The targeted system cannot handle any incoming requests due to the breach, and the entire system crashes [9]. In such assaults, the hacker compromises thousands of hosts, including PCs, servers, and IoT devices, and exploits them all at once to overwhelm the victim's resources with traffic. This traffic suspends network users and defending against it is quite difficult [10]. A system administrator can detect a potential threat but cannot handle the simultaneous attacks in real-time. As a result, the SDN controller must be subjected to particular security constraints. Additionally, automatic attack detection systems are required to ensure an optimum level of service quality and to detect suspicious intrusions [11]. The detection of DDoS attacks includes various challenges, including identifying the characteristics of attack traffic; lack of cooperation among the nodes of a coherent network; modification in attack tools; usage of a fake IP address; and short attack duration and limited response time [12]. To deal with these issues, we need a system that can detect intrusions and store data in communication while ensuring data integrity. The ultimate purpose of the intrusion detection method is to identify intrusion before it causes damage to a targeted server and to stop attacks as close to their source as possible [13]. Machine learning (ML) techniques provide a framework frequently employed in VANETs to evaluate large amounts of data and generate efficient rules for attack detection, classification, and prediction [14].