# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 1 Introduction
The ML approaches apply not only to intrusion detection but also in fields such as speech recognition, recommendation systems, medical diagnosis, and traffic prediction. It also offers a desirable approach for detecting intrusions in VANETs ahead of time with acceptable speed and accuracy [15]. There are four different forms of communication in VANETs. * In-vehicle communication: the in-vehicle domain is becoming increasingly crucial in VANET research. Driver and public safety are both improved when in-car communication systems can detect issues such as driver fatigue and drowsiness. * To better assist drivers, V2V communication can facilitate the exchange of information and warning messages between vehicles. * Virtual-to-individual (V2I) communication in VANETs is an emerging research area. Through vehicle-to-infrastructure (V2I) communication, drivers can access real-time traffic and weather updates and participate in environmental sensing and monitoring. * Wireless broadband technology, such as 3G/4G, may be used for vehicle-to-vehicle (V2V) communication. With more traffic data, monitoring information, and entertainment options available in the internet cloud, this type of connectivity will be beneficial for active driver assistance and vehicle tracking. The primary goal of public safety technology is to improve the overall safety of transportation systems. Real-time traffic conditions, post-crash alerts, traffic monitoring, collision mitigation, and other safety applications are available. Non-public security applications aim to improve driver comfort by offering value-added information, such as nearby petrol stations, hotels, workshops, and restrooms, among other things. It is often advisable to use public safety technology than non-public safety technology. Numerous applications are used in many nations, including Germany, Europe, and Switzerland, to make roads and automobiles safer. Convenience applications are mainly used to handle traffic congestion scenarios, such as available parking spaces, route diversions, and electronic toll collection, among other things. New elements to the services mentioned above that focus on converting traffic jam time into constructive work, such as reading on the internet while waiting in a vehicle for someone, are known as productive activities. Additionally, fuel is saved when the average toll booth wait time is decreased to 1-2 min [16]. The following are the main contributions of the current study:  
* The primary objective of this paper was to use a machine learning-hybrid optimization strategy to detect DDoS attacks and allocate bandwidth within the VANET. * Algorithms and models based on machine learning can support robust communication analysis and resource management, as well as the increasing communication and computation demands of emerging networking applications. * Security-based modified SVM with HHO technique was utilized to identify DDoS attacks. The SVM-HHO technique demonstrated the most outstanding feature selection and parameter optimization capabilities compared with other well-known metaheuristic algorithms. Using H-WDFOA, the impact of a DDoS attack on the VANET was diminished (H-WDFOA-VANET). * The combination of DF optimization and WOA increased bandwidth. The condition was repeated until all available bandwidth had been allocated. The H-WDFOA was utilized to achieve effective bandwidth allocation and optimal vehicle routing. * For the stability of the vehicular network, swift and efficient communication was established once the predicted information had been validated. * Utilizing performance indicators such as energy consumption, throughput, latency, and packet delivery ratio, the performance of the proposed method was evaluated. The implementation was accomplished using MATLAB software.