## 2 Literature Review
The tool works in combination with NS-2/3. In [29], the authors proposed a network simulation for VANET malicious node detection under NS-2 and SUMO. SUMO imports network models for related matters. The recent SUMO projects are VABENE, iTEIRIS, and CityMobil. Researchers in [30] created the GUI-based road network with publicly available tools for the simulation in VANET. Inside the VANET, another tool that is introduced is OMNET++. OMNET++ is used for GUI-based communication. It uses C++-based architecture and provides the effective delivery of network services. The simulation and development tool helps to integrate the network connections. Authors in [34] use OMNET++ to detect DDoS attacks. Four simulated vehicles are used as a study for the research topic in this network. In DDoS, more than one attacker attacks the system. The authors use multiple attack vehicles as a zombie. Another simulation by [PERSON] et al. [35] uses RaeSE and OMNET++ under the attack development using the web servers. Simulated DDoS attacks were generated using random access to the network effects. In this technique, multiple routers and factors are involved in addressing the issue raised in this technique. The network access ratio and packet drop ratio are observed in this technique. There are several design parameters considered for proper environmental protection. After the malicious network node attacks, the Wireshark analysis tool is used to analyze the network. The results are quite remarkable in terms of providing effective network services. Malicious node attackers are the threat to the VANET that provides the ability to communicate and handle the network environment. The malicious node attacks incorporated in the network, when intercepted with normal vehicles, are not encounters. The attacker encounters that the packets are captured, doped, and modified in the network. According to research in [36], the attackers attack the network using OMNET++ and SUMO. VEINS is also part of the attack environment. The maps are defined using VEINS, and other tools incorporate the network. The total of different cars and other network environments are incorporated with the network disability and efficiency. According to the study, fleet-based and distributed attacks are encountered in the study environment. The DDoS attacks come from flooding vehicles or RSUs. The authors in [37] proposed the graphical aware flooding technique in VANET. It is similar to a technique for normal flooding attacks. The packet drop ratio is increased in this type of flooding attack in the network. The vehicle behind the vehicular communication operates in this favor of the flooding attacks for the order of the flooding. Another method named OLSR (Optimized Link State Routing) is proposed in which vehicles propose a new technique for the similarity and other main features related to the network architecture. Flooding is one of the critical methods for obtaining advanced-level communication between vehicles. Many machine learning methods are used in this subject to detect and provide DDoS, Sybil, and alert message detection techniques. The attacker wants to obtain the communication pattern to provide an effective communication environment. The authors in [38] use the case to detect the malicious nodes using machine learning models such as logistic regression, KNN, decision tree classification, random forest, and bagging. The authors check that the decision techniques used effective learning and mitigation techniques for the final approval of the detection of misbehavior detection. The accuracy of the network is achieved and provides effective qualitative research patterns for the selection and provision of the results. The authors in [39] proposed a hybrid, trustable, deep learning model to detect malicious nodes in VANET. Communication in VANET is expensive when nodes are detected and provide efficient and effective qualitative-based trust management approaches.