## 3 Related Work
[21] explored how to model an IDS based on a deep learning approach using recurrent RNN-IDS on the NSL-KDD dataset. They compared this approach with J48, artificial neural network, random forest, SVM, and other ML classifiers proposed by previous researchers on the benchmark dataset. They reported that RNN-IDS had superior accuracy compared with traditional ML classifiers. [PERSON] and [PERSON] [4] evaluated 10 ML algorithms on the NSL-KDD dataset with and without the feature selection/reduction technique using the Weka tool. They reported that random forest, bagging, PART, and J48 were the best-ranked classifiers without using the feature selection/reduction technique, but they consumed much time in building the model. The authors subjected these four classifiers to further evaluation using feature selection/reduction methods to reduce the model building time while achieving high intrusion detection accuracy. They concluded that the feature selection/reduction methods significantly reduced the model building time without compromising the detection accuracy of the tested classifiers. They also believed that feature selection/reduction helps a number of ML classifiers perform well. Thus, researchers can assume that the results and feature selection/reduction methods of this study are promising for the investigation of more ML classifiers to determine the best classifier with the highest detection accuracy and lowest model building time, which can satisfy the real-life need to build an efficient network IDS. [PERSON] et al. [22] presented an IDS framework with feature selection within the NSL-KDD dataset; the framework was based on dividing the input dataset into various subsets and combining them using the information gain filter in the Weka tool. The authors showed that the feature selection methods used improved detection accuracy and decreased complexity. They also demonstrated that the highest intrusion detection accuracy was obtained when using the random forest and PART classifiers under combination methods of the product probability rule. [PERSON] et al. [23] proposed a combination of the feature selection method, normalization, and SVM using Weka's modified rank-based information gain filter to select 17/41 NSL-KDD dataset features. They achieved an overall detection accuracy rate of 99.8%. [PERSON] et al. [24] proposed an IDS framework based on the feature selection and ensemble learning techniques. They used a hybrid approach by combining CFS with the BA algorithm to select the optimal subset based on the correlation between features. Then, they formed an ensemble that combines the C4.5, RF, and ForestPA classifiers and applied these on the NSL-KDD, AWID, and CIC-IDS2017 datasets. They demonstrated that the proposed CFS-BA-ensemble approach showed better performance than the other related approaches. Notably, these previous studies emphasized detection accuracy but not the time taken to build the model. Furthermore, [PERSON], [PERSON], and [PERSON] [25] evaluated and compared the performance of six supervised ML classifiers, namely NB, logistic, multilayer perceptron (MLP), Sequential Minimal Optimization (SMO), IBK, and J48, with the full NSL-KDD using Weka software. They investigated this in the context of intrusion detection along various dimensions, mainly feature selection, sensitivity to hyper-parameter tuning, and class imbalance problems. They used accuracy, TPR, FPR, precision, recall, F-measure, and receiver operating characteristic (ROC) area as the parameters to evaluate the performance of the tested classifiers. They also used a PC installed with Intel(R) CORE(TM) i5-6600 KCPU @ 3.50 GHz, 3.50 GHz, 8 GB RAM running a 64-bit Windows 10 OS, x64-based processor to carry out the experiment protocol. They concluded that J48 and IBK are the two best classifiers in terms of accuracy detection, but IBK was much better when applying feature selection techniques.