## 2 Related Work
### Literature Review of Feature Selection Methods for Intrusion Detection Systems
The intrusion detection framework, which utilizes a feature selection process and ensemble learning techniques, was proposed by [PERSON] et al. [28]. Initially, for dimensionality reduction, the CFS-BA algorithm was performed to choose the best subset by using the correlation between properties. After that, an ensemble method, which combines random forest, C4.5, and Forest by Penalizing Attributes algorithms, was carried out. At last, voting was employed to combine the probability distributions of the base learners for intrusion detection systems. The proposed framework was tested on the NSL-KDD, CIC-IDS2017, and AWID datasets. According to the authors, the test results revealed that the presented CFS-BA feature selection algorithm outperformed state-of-the-art approaches based on various metrics. The proposed framework also needs to be tested on zero-day attacks, which are occasionally seen in communication networks. [PERSON] et al. [29] explained intrusion detection systems for wireless sensor networks using dynamic feature selection and fuzzy temporal decision tree classification. The featured selection method identified the ideal subset of features within the dataset. After feasible features were selected, fuzzy temporal decision tree and convolution neural networks were combined to separate intrusions from the normal network traffic. The proposed method was tested on the KDD Cup dataset as well as the network trace dataset. According to the paper, the proposed method effectively detected known and unknown intrusions with less energy consumption. Moreover, it increased the packet delivery ratio while decreasing false-positive and -negative rates. The proposed approach was tested on the KDD Cup dataset, which consists of redundant features and does not consist of new intrusions. Thus, the proposed approach must be tested on new datasets to measure the effectiveness of the suggested method more correctly. [PERSON] and [PERSON] [30] suggested TS-RF (Tabu search-random forest) which uses a wrapper-based feature selection method. In the proposed approach, Tabu search was performed as a search strategy whereas random forest was used as a learning algorithm. Tabu search is a kind of local neighborhood search in which each possible solution has an inter-related set of neighbors. On the other hand, random forest is a combination of tree estimators that consists of many trees and classifies using features from which each tree is sampled independently. To show the effectiveness of the proposed method, the presented technique was tested on the UNSW-NB15 dataset. According to the paper, the proposed feature selection method improved the accuracy rate while decreasing the false-positive rate when compared to other feature selection approaches. The UNSW-NB15 dataset has a class imbalance problem, which increases the misclassification rate while decreasing the detection rate. Before applying the feature selection method, the class imbalance problem needs to be solved. In addition, other new machine learning algorithms such as deep learning can be used instead of random forest. Tabu search can be modified to find more relevant features. [PERSON] et al. [31] proposed a hybrid approach to select features and detect anomalies in a computer network. They used an information-gain-based algorithm on the NSL-KDD dataset for feature selection. For the classification stage, the Support Vector Machine as well as the Optimization Cuckoo Search algorithm were used. The proposed algorithm was tested on the NSL-KDD dataset. According to the test results, the proposed method generated high accuracy and outperformed other methods that had applied to the NSL-KDD dataset. The proposed method was only tested on the NSL-KDD dataset, which consists of old attack types. Thus, the proposed method needs to be tested on the new intrusion detection datasets. Moreover, the obtained accuracy result, which was 94.21%, is not very high for current IDSs, so feature selection and optimization algorithms must be improved further. [PERSON] et al.