and deep learning-based solutions to network intrusion detection problems [8, 9, 10, 11]. Our research employed both techniques to reduce the data dimensionality. In contemporary research, the following machine and deep learning techniques have been used extensively for detecting network traffic anomalies: Decision Tree [12], XGB-Boost [13], Support Vector Machine [14], Deep Neural Network [15], and Convolution Neural Network [16]. In addition, [PERSON] et al. [17] mentioned that many researchers use ensemble techniques to achieve optimized performance in the network intrusion detection domain. We took advantage of the power of the Ensemble learner after exhaustively removing unuseful features. Our work classifies normal and attack traffic. To the best of the author's knowledge this combination of algorithms for feature selection, feature extraction, and classification has not been used before. In this study, we propose a hybrid dimensionality reduction module that harnesses the power of feature selection and feature extraction approaches to reduce the input feature space since unuseful features increase computational complexity and decrease accuracy. [PERSON] and [PERSON] [18] compared three feature selection techniques: mutual information gain, the chi-square method, and recursive feature elimination. They reported that recursive feature selection selects optimal features compared to other techniques. Therefore, we used the Recursive Feature Elimination method in this research. This method selects the twenty most useful features from the input feature space. These selected features are given to a feature extractor that transforms them into components using the Principal Component Analysis technique. We chose Principal Component Analysis (PCA) as it is faster and since the first few principal components are computed and more interpretable than other techniques, such as Auto-encoder. We used an Ensemble learner for the classification task as it performs better than individual learners in most cases [19]. It does hard voting on individual predictions of SVC, KNC, and DNN classifiers and outputs the majority vote. We chose individual models from the machine and deep learning domains. We aimed to apply diverse models to our dataset instead of similar types like different tree-based algorithms. SVC is used because it can classify linearly and non-linearly separable data points. KNC is chosen because it clusters similar data points. Our data has different attack classes; therefore, it is anticipated we will achieve good evaluation results using it. DNN is also employed due to its ability to successfully classify non-linearly separable data points. Classification is performed on the full dataset, twenty top-ranked features, and 10, 12, and 15 principal components. The evaluation results reveal the effectiveness of the proposed technique. Compared with a full feature set, the classification accuracy does not significantly impact when a reduced feature set is used. The main contributions of this paper are the following:  
* A hybrid dimensionality reduction module is developed that performs its job in two phases. The first phase is feature selection, which selects the valuable features based on their rank using the Recursive Feature Elimination method. The second phase is feature extraction, where selected features are transformed into principal components by applying the Principal Component Analysis (PCA) technique. * Classification is performed using SVC, KNC, and DNN to classify attack and normal traffic. The result of these classifiers is fed into the Ensemble learner, which produces final predictions based on the majority vote. * The proposed research is compared with other contemporary research where the same dataset is used, and dimensionality reduction is performed before giving the input to the classifier. The experiment's results show that our scheme has better classification accuracy. The remaining part of this paper is structured as follows. Section 2 discusses contemporary research in this area of research.