## 2 Literature Review
### Machine Learning-Based Approaches for the Identification of Malicious Nodes  
Various machine-learning techniques have been applied to identify malicious nodes within the existing body of research. The work presented by [27] introduces a trust-based machine learning approach employing the k-nearest neighbors (KNN) algorithm and fuzzy logic for clustering. Trust is ascertained using the beta distribution method based on three fundamental values: trust, reputation, and reputation dependency. Each node's reputation is intricately linked to its level of trustworthiness. Following the trust evaluation, the derived trust values are transmitted to the cluster head, which subsequently updates the reputation values of individual nodes. Upon completing the clustering and trust assessment, the presence of malicious nodes is discerned. This is achieved by having the source node dispatch a route request to the cluster head, scrutinizing the destination node's presence within the cluster. If the destination node is not located within the cluster, the cluster head initiates communication with the corresponding cluster head in other clusters. The evaluation of trust and reputation for each node is meticulously conducted, and any indications of malevolent activities prompt the blocklisting of the respective node. In a distinct study by [35], machine learning techniques and statistical models are leveraged to identify spurious nodes. The proposed methodology evaluates the efficacy of various classifiers in pursuing malicious node detection. These classifiers include the gradient boosting classifier, support vector machine, k-nearest neighbors classifier, logistic regression, and naive Bayes classifier. From the pool of 19 extracted features, the top 7 features are selected for evaluation, encompassing parameters like throughput, lost packets, source node IP address, source and destination port, time of the first packet received, and time of the last packet received. The routing protocol adopted is AODV, and the NS-3 simulator is utilized for the experimental framework. The gradient boosting classifier emerges as the most effective choice for malicious node detection. Ref. [36] introduces a misbehavior detection technique founded upon a machine learning algorithm. This approach unfolds through a sequence of four key steps. The first phase involves data acquisition by collecting information from sensors such as GPS and kinematics. Subsequently, various features are extracted from the acquired data. In the second phase, data on neighboring nodes is gathered, and specific characteristics of counterfeit nodes are isolated. These attributes include node behavior, packet communication delay, and broadcasting ratio. The third phase focuses on feature derivation and model training using supervised learning techniques. Once the model is suitably trained, malicious nodes are identified by applying an ANN classifier. Ref. [37] introduced a machine-learning approach to detect malicious nodes within the mobile ad hoc networks (MANETs) context. This innovative approach couples the AODV protocol with ANN and support vector machine (SVM) techniques to bolster routing security. SVM is leveraged to identify abnormal behavior exhibited by nodes encountered along the path established by AODV. Upon singling out a suspicious node, ANN is deployed to assess the accuracy of SVM outcomes, thereby ascertaining whether the flagged node is fraudulent. The existing body of literature delves into many techniques to enhance the efficiency and security of routing. VANET clustering techniques are employed diversely to optimize routing efficiency and prolong network lifespan. The amalgamation of fuzzy logic with AODV and various AODV routing strategies is explored to make informed routing decisions. In parallel, AODV is harmonized with distinct machine learning methodologies and conventional practices to identify and combat black hole attacks in vehicle-to-vehicle (V2V) communication scenarios.