## 2 Literature Review
The MSODL-ID approach accurately recognized different types of attacks by providing outstanding results [10]. Geometric SMOTE was used to generate different rare-class attack data while keeping sample feature similarity using the enhanced kernel density estimation algorithm. Soft-voting ensemble learning was used to detect multi-class anomalies in balanced and dimensionally reduced data [11]. In another article, an ID based on DL for IoT networks, employing a four-layered deep FC network architecture, was used to detect malicious traffic. The proposed system was implemented in Python using the TensorFlow library and was tested using the CNS and COS. The suggested system updates on classifier-discovered features. The feature extractor module intercepts data packets and extracts features such as Id, Tr, Rr, and TRR, where Id refers to 'identifier', Tr refers to 'Transmission Ratio', Rr refers to 'Reception Ratio', and TRR refers to 'Transmission Reception Ratio' [12]. The authors of another article chose OBPNN to monitor sensor data and assess health, optimizing the SVM with the FSA. The suggested automated health status evaluation removes outliers using kernel density estimation, determines the appropriate bandwidth for typical data, simulates normality using OBPNN, and calculates the health score based on the decision function and distance from the separation hyperplane [13]. An effective and smart NIDS built on DL was suggested in another article. The model's performance was evaluated using various classification techniques and achieved a 100% and 99.64% accuracy rate when trained and tested with the datasets, respectively [14]. Another study presented an AutoML model that uses Bayesian optimization to automatically choose an ML model and adjust its hyperparameters, where 'k' barriers are needed for accurate detection and prevention of intrusion. The ML models considered in the study included various regression techniques along with bagging, ensemble, and boosting ensemble learning [15]. In another study, a decision tree was built using the CSA, and the selected features were represented by the terminal nodes of the decision tree. The proposed IDS was compared to other techniques using the CIC-IDS2018 benchmark dataset. The suggested approach is a hybrid decision-making framework for classification and feature extraction using a decision tree with a maximum information gain ratio. The KDDCup 99 dataset was used for training and testing the proposed methodology [16]. In another study, a hybrid method that combines the RF model with the Pearson correlation coefficient was devised for efficient and effective feature selection. The categorical features were transformed into numerical values using an encoding approach. When tested in actual network settings, the decision tree algorithm proved to be the most effective and accurate method for ID [17]. In order to identify network intrusions using a multi-model ID system, the paper presented a method based on feature selection and majority vote. Six distinct ML methods were used for training and assessment. A multi-model categorization system was developed using the Majority Voting approach [18]. An effective ML-based approach for WSN routing and clustering was presented in the article. In order to transmit data, identify nodes, detect faults, and conduct analyses, WSNs employ ML algorithms such as the RF algorithm, MLPNN (Multi-Layer Perceptron Neural Network), and RBFNN [19]. In another study, ML-based IDS was implemented to keep an eye on traffic in vehicular ad hoc networks (VANETs) and spot any questionable behavior using streaming engines for big data analytics. Using RF as the data classifier during training and including real-time data management, visualization, and collection, the IDS framework attained maximum accuracy. Additionally, it assessed the effect of the IDS on network throughput, showing that it is a resource-efficient solution [20].