## 2 Literature Review
The authors proposed a hybrid technique using deep learning to overcome challenges such as packet loss, packet damage, routing difficulties, and software and hardware failure issues. The attacks are classified inside the VANET environment. A hybrid algorithm is used for the CH selection and hybrid optimization approaches. The optimization algorithm is used to provide the effective classification and methodology for the proper certification. In the end, 94% accuracy is achieved in detecting the malicious nodes from the VANET environment. In [40], the authors target traffic exchange and communication in VANET. DDoS attacks are considered in the network. An intrusion detection system is one possible way to handle these attacks, but the growing needs of larger networks challenge the environment. The author uses the random forest algorithm for the posterior detection of malicious nodes using high accuracy. The detection accuracy is enhanced by providing these effective networks and other related matters to detect and classify DDoS Attacks. The proposed model enhances the machine learning results with detection accuracy and value predictions. VANET is always available for attackers' malicious activities due to its open nature and provides many different techniques that detect and classify malicious nodes. The authors use multiple machine learning models to detect the attacks from the system. Different machine learning algorithms detect every attack under binary classification. The accuracy shows that the attack detection technique effectively handles malicious node detection. The main limitation of the work is that this does not handle the real-time detection of malicious nodes. This means that the detection accuracy is compromised when a real-time scenario is involved [41]. Another technique proposed by authors in [42] is to detect malicious nodes as position falsification attacks. The authors consider cooperative intelligent transportation systems (C-ITS) to connect advanced technology with useful features and enhanced required terminologies. The attack is dangerous for the safety of the passengers and other related situations. It plays a vital role in detecting and mitigating the vital attack system in VANET. The authors consider three features: sender position, existence, and performance. KNN and RF models are used in this system to detect and mitigate the research-based parameters related to the advanced level significance in this regard. The results effectively show better results, but the limitation is the real-time detection of the malicious nodes under different machine learning models. Malicious node detection in VANET is challenging, especially when working under a wireless network. Additionally, network situations such as real-time detection make it more difficult to detect malicious activities from the network. The misbehavior detection/DDoS detection technique is considered in this research. The technique observed that very little work is performed under the VANET environment. The attacked scenarios are considered on multiple intensities. Table 1 shows the summary of existing techniques with working parameters compared with existing ones.