## 1 Introduction
### Research Contributions  
The main contributions of the research are to detect malicious attacks using machine learning techniques. * The research supports the real-time communication between vehicles in VANET to detect malicious node detection. * The proposed multi-layer classifier technique, called the DDoS detection scheme, detects malicious nodes that hijack a vehicle in the network using a monitoring approach. * The machine learning models are evaluated to adopt real-time malicious node detection with OMNET++ and SUMO simulations under a higher accuracy of 99%. * We design a cluster-based VANET architecture in which the RSU handles and implements the real-time machine Learning algorithms to detect the malicious nodes. This technique is conducive to overcoming the malicious activities of DDoS attacks in VANET. * The proposed technique predicts real-time DDoS malicious activities by continuously monitoring each vehicle in the network using an ML algorithm. * The real-time dataset is monitored to evaluate each node through the RSU-based technique for the performance evaluation and enhancement of parameters. * The ML-based model is trained using real-time traffic analytics in VANET after observing each node's behavior, message threshold, speed, packet routing, and network congestion and handling parameters. After the evaluation and dataset of these parameters, the ML-based algorithm is trained and tested in a real-time environment through a simulation setup. * The anomalies in the network are checked, and malicious vehicles are removed from the network using a RSU-based real-time checking mechanism. * We design a real-time malicious node detection mechanism to detect the DDoS attack nodes from real-time analysis to perform the selected performance based on a generated dataset under the severity of 10% to 70% under attack. * The performance is compared with the existing state-of-the-art techniques using machine learning on a training and testing dataset model that is real-time generated under the supervision of RSU that implements the machine learning models. The network throughput and model prediction accuracy are achieved using the mentioned requirements. * The machine learning approach for DDoS-based malicious node detection and classification. * The network throughput is achieved using machine learning techniques. * False Positive Rate (FPR), True Positive Rate (TPR), False Negative Rate (FNR), and True Negative Rate (TNR) for malicious node detection and classification. * The research contributes towards the modern ITS-equipped vehicles from the vehicular industry for classification and model prediction accuracy and secured medical data. Section 2 describes the thesis background with respect to related work. We define misbehavior detection in VANET. Close related work is described in this chapter of the thesis. All the related work is effectively described and utilized in these strategies. At the start of this chapter, we define the structure, and then machine learning-based malicious node detection is determined, which provides the fully reference-based allocation and development schemes. Section 3 contains the proposed method to detect real-time malicious nodes using machine learning and provide significant results using such techniques. The neural networks are introduced with the ability to handle and provide the significance needed to provide effective resources. The three state-of-the-art algorithms are defined in this chapter. The proposed model with methodology diagram is implemented in this context to define the clear features of the proposed technique. The workflow model for the proposed methodology is also explained to detect malicious nodes using these techniques. In Section 4, we simulate our research ideas using simulation tools. We adopted machine leaning and VANET model selection to deliver these services successfully. The results are gathered, and then a discussion is performed using such techniques. The analysis and ratios are performed using these techniques.