## 2 Literature Review
The approach was used by MANET, although it is also applicable to VANET. The method is commonly utilized for sending fake packets to discover the AODV path. Since a Black Hole rarely searches the routing table first before the requested node is returned, a Black Hole is caught by responding with its request to the false IP address that has not been found in the system. This method has been used to identify solo and joint Black Hole assaults. The author established an automobile monitoring procedure. Vehicles are placed together in their solution into several clusters lead by the cluster head (CH), which in each cluster is the most dependable automobile. Whenever a new car is added to the cluster, the verifier begins scanning for information about the new vehicle's characteristics and actions. It notifies CH when the verifier observes that the van often drops packages; otherwise, it does nothing. CH therefore decreases the trust value of the car and reports this incidence to its neighbors. When the trust value of a vehicle is below a certain threshold, CH alerts a certificate authority (CA). The automobile is placed on the Black List by the CA. It then alerts all the cars that it stops connecting within the Black Listed node. The testing findings show that the proposed strategy can detect malicious intruders even when they move quickly [27]. The preventive technique introduced in [27], and the verifier selection is refined in [28]. The researchers in [29] further expanded the strategy given in [27], including a methodology used to eliminate and isolate from the entire network a Black Hole. The procedure presented in [28] is identical to the approach proposed in [27]. It is distinguished by the additional parameter used to isolate the attacker and the alerts created by the identification of the malicious node that is spread around the networks. When compared to [24], the suggested method was more effective in preventing and detecting attackers with high mobility. The authors in [26] improved the technique of detection introduced in [27] by increasing the number of checks to run, which is determined by the load, the distance between checks and the suspicion score. When comparing simulation results to those given in [27], it has been discovered that performance metrics have improved. Regarding MANETs, a method was developed for recognizing nodes with a Black Hole or Gray Hole, which was published by researchers [30]. The authors used the AODV MAC layer to execute their security measures. The team (Cseq) created two new control packets: the Response sequence (Rseq) and Code Sequence (CodeSeq). To find a route and gain access to a channel, a source must first send Cseq to all of its neighbors, and that neighbor must then send Rseq to all its neighbors. A link to a network layer is formed if both the Cseq and the Rseq of a given neighbor match; otherwise, that neighboring node is rejected by the source node, and others are informed that this is a dangerous node. The researchers utilized an IDS based on information (datasets) from trace files that were kept in a database by executing the Network Simulator 2 (NS2) code in a VANET environment. Trace files have been broken down into three categories: the basic trace, the trace of internet protocol, and the trace of AODV. In order to estimate the proposed solution, the features obtained from the trace files were then employed to inform future developments. When determining if a vehicle's behavior is malevolent or normal, these characteristics were utilized as the determining factor. For the purpose of feature extraction, a statistical approach known as Proportional Overlapping Scores was employed (POS) [31]. The above studies proposed different security techniques to deal with multiple kinds of attacks in Black and Gray Holes. Moreover, recent works also considered the detection of malicious nodes. Different encryption techniques such as hashing with SHA-256 were also considered to secure the communication.