## 2 Literature Review  
The nature of the vehicular networks is open, which means it uses wireless technology to communicate under V2V, V2I, and I2I communications. The openness makes it vulnerable to cyber-attacks, such as DDoS attacks. Regarding the wireless network's security, some solutions are provided in this context--the literature review defined the machine learning-based DDoS attack detection using real-time scenarios. According to the survey, we studied around 50 articles on machine learning-based malicious node detection using the real-time scenario. In this chapter, we focused on the real-time scenario and found the research gap which we focused on in this research. The existing techniques for malicious node detection in VANET are studied [26]. Understanding VANET security architecture is important at the initial stages of the literature before moving on to the final decision-making. In [27], the authors write a review article to discuss VANET security. The authors review almost 114 articles on VANET security. The VANET is a soft target for attacks, such as DDoS, eavesdropping, bogus information, impersonation, and hardware tempering. The DDoS attacks are considered to target resolving issues using machine learning. In [28], the authors define the security mechanisms, such as RSA, public key encryption, and elliptic curve cryptography. These security mechanisms play an important role in the development of effective mechanism handling. In this paper, the authors continuously highlight DDoS detection in VANET. The authors in [29] propose a new security protocol that safeguards against DDoS attacks in VANET. The main steps of the proposed solution are as follows:* DDoS detection;
* DDoS reporting;
* DDoS investigation. The DDoS handles use the activities to detect the DDoS from the literature ordinance values. DDoS is also a form of misbehavior detection. Initially, the system detects the misbehavior, and then this detection is reported to the authority named misbehavior authority. The authority then completes the detection and provides notification about the DDoS attacks. In another research article, the developers develop a new system to develop the new misbehavior system. Another research in this regard is published in [30], in which a misbehavior reporting system is designed to develop a new system. The messages are contained from vehicles that show or misbehave in the network. The evidence containers contain vehicular information from its historical behavior. This affects the simplicity of the network and promotes network operations. The detection inside the system contains the detection containers able to manage the network operations. This container contains information regarding the errors that are reported in this context. The dataset generation and misbehavior detection from the network is a challenging task--authors in [31] design VeReMi (Vehicular Reference Misbehavior Dataset). The authors highlight the issue related to misbehavior detection and other dataset generation systems. Different datasets are generated, and studies define the misbehavior network inside these challenges. VeReMi uses log files to detect misbehavior whenever the network starts behaving as if it is engaged in malevolent activities. In this network, the source code is for about 255 simulation rounds. It detects attacks such as network traffic and attacker densities for network analysis. In [32], the author makes claims about these kinds of datasets. According to researchers, the authors achieved great achievements, but the dataset is imperfect. Through this technique, the users cannot detect multiple attacks--the author's continuous development of network architecture using the VeReMi dataset. The dataset has less impact than other dataset techniques. Another technique to simulate the VANET, SUMO (Simulation of Urban Mobility), is proposed. SUMO is used in multiple research activities. It is an open-source simulation for VANET traffic simulation. The authors in [33] use the study of SUMO in VANET.