## 2 Related Work
### Literature Review of Feature Selection Methods for Intrusion Detection Systems
[32] presented an intrusion detection system that uses ensemble feature selection and classification in the cloud environment. They used a univariate ensemble filter feature selection method to separate the most significant features from the datasets. During the feature selection, five filter feature selection methods were obtain to get the best subsets. To obtain the final subset, rule combinations were used too. After features were selected, voting techniques were used for classification. Classifiers including decision tree, logistic regression, Naive Bayes, and Support Vector Machine were used for classification. The presented method was tested on NSL-KDD, Kyoto, and real-time datasets. According to the test results, model performance was increased compared with other existing methods. The obtained results are statistically significant based on the pairwise t-test. Furthermore, the model achieved a higher accuracy rate while decreasing false alarms. In the paper, the authors did not mention how they handled unknown attacks or how their method was different from existing techniques. Also, deep learning can be used as a classifier to distinguish intrusions from normal network traffic. Cyber-physical systems (CPSs) constitute intricate multi-layered structures that underpin critical global infrastructure, exerting substantial influence on human lives [33]. With the growing emphasis on connectivity within CPSs, the importance of cyber security has surged. [PERSON] et al. [33] introduced a metaheuristics-based feature selection model for IDSs in CPSs. They tested how effective feature selection improves the performance of different machine learning approaches. They introduced the utilization of F1-Score within the adapted Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to elevate intrusion detection performance across binary, multi-class, and expert classifiers. The findings from numerical analyses unveil that distinct feature subsets align more favorably with specific combinations of IDS strategies, classifier algorithms, and attack classes. Notably, the GRASP metaheuristic identifies features that effectively discern four classes of Denial of Service (DoS) attacks and numerous iterations of injection attacks in the realm of cyber-physical systems. [PERSON] et al. [34] proposed a feature selection method grounded in multi-level correlations that was tailored for network intrusion detection data. The suggested technique chooses significant features while reducing the size of the analyzed data. First, they normalized the data. Then, they used correlation-based feature selection in a multi-level manner. Finally, they applied classification. The method's effectiveness was analyzed on the UNSW-NB'15 dataset, for both binary and multi-class cases, showing its prowess. Experimental results demonstrated its superiority over existing techniques. [PERSON] et al. [35] suggested an innovative methodology for feature selection and extraction in the context of anomaly-based intrusion detection systems (IDSs). The proposed approach utilized two entropy-based techniques, namely information gain (IG) and gain ratio (GR), to select and extract pertinent features at varying ratios. It involved leveraging mathematical set theory, specifically union and intersection operations, to extract the optimal feature set. The method was evaluated on two datasets, the NSL-KDD dataset and the IoT Intrusion Dataset 2020 (IoTID20), which employed four machine learning algorithms: Bagging, IBk, J48, and Multi-Layer Perception. The proposed approach successfully identified 11 and 28 relevant features out of 86 using intersection and union approaches, respectively, on IoTID20. Similarly, the NSL-KDD dataset identified 15 and 25 relevant features out of 41 using the intersection and union strategies. Through a comprehensive comparison with other state-of-the-art investigations, the proposed model demonstrated its superiority and competence, achieving an impressive classification accuracy of 99.98%.