# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 2 Literature Review  
[PERSON] et al. [17] proposed a trust-based methodology for determining the distributed denial of service (DDoS) attack in VANET. The residual energy, trust policy, data factor, statistics, such as frequency value, and true hypothesis were used to create the trust elements. These trust elements were utilized to generate the trust evaluation matrix. The deterrence design of the trust evaluation approach was incorporated with a clustering method to identify the attacker in a proficient manner. For identifying DDoS assaults on VANET, [PERSON] et al. [18] used a hybrid technique. These hybrid approaches were developed using ANOVA Dot and RBF Dot SVM kernel methodology. Features such as collision, jitter, and packet drop were used to replicate a real-time network communication scenario. As a result, the hybrid algorithm-based model was superior at detecting DDoS attacks. To identify DDoS assaults on VANET, [PERSON] et al. [19] devised a multivariant-stream analysis (MVSA) method. The network was protected from distributed denial of service attacks by keeping multiple levels active. This tactic's success hinges on the ability of individual cars to talk to one another through the Road Side Unit. Using big data technology, [PERSON] et al. [20] provided a method for detecting DDoS attacks. The real-time network traffic-collecting module and traffic-detection module are two components of the described technique. In addition, spark and HFSA-VANET were also used to speed up data processing and store significant suspicious attacks. [PERSON] et al. [21] suggested a machine learning approach that combined traditional methods with more modern ones (IDS) to boost the effectiveness of intrusion detection systems. The IDS was a potential tool for thwarting VANET attacks in advance. Consequently, the coreset was utilized to enhance detection accuracy, and the random forest (RF) was implemented to boost IDS functionality. For VANET, [PERSON] et al. [22] implemented a centralized routing mechanism with mobility prediction (CRS-MP), using a software-defined network (SDN) controller. The Road Side Unit (RSU) or Base Station (BS) determined the average latency and transmission probability of each request from the vehicle based on the mobility forecast. Information was gathered by the SDN controller via RSU, whereas the BS was viewed as mere switches. [PERSON] and [PERSON] [23] presented VANET's PML-CIDS. In this study, the authors used the alternating direction multiplier method (ADMM) to a set of empirical risk minimization (ERM) problems. Differential privacy was used to acquire the PML-CIDS privacy notation, and the NSL-KDD dataset was used for the evaluation. An improved VANET routing model was developed by [PERSON] and [PERSON] [24], which accounted for congestion, travel, collision, and QoS awareness cost. An updated pricing scheme was developed in light of the restrictions. When calculating the optimal route, the authors considered the total cost and quality of service (QoS) component, which was fuzzy-calculated. The optimal routing in VANET was achieved at the lowest possible routing cost by developing a novel RI-CS optimization approach, which was built by fusing the ideas of ROA and CS. To improve the effectiveness of the routing procedure in VANETs, [PERSON] al.proposed a new routing protocol, EHACORP, which was presented in [25]. At EHACORP, the combination of DCM and SBACO shortened the time it took for ants to find their way around, increased the rate at which they converged, eliminated the need for blind broadcasting packets, expedited packet processing, and prevented bottlenecks. EHACORP increased its packet delivery ratio (PDR) and throughput by avoiding bottlenecks, rapidly processing packets, and quickly converging. In [26], the authors presented a study on the split delivery vehicle routing problem that arises in the distribution of fresh agricultural products.