## 2 Related and Background Work  
In this section, we discuss the existing research in the domain of intrusion detection. We briefly also discussed the formal methodology, algorithms and results achieved by various researchers from research previously conducted. In this paper, [PERSON] et al. [14] proposed a model for intrusion detection. The KDD dataset was used for experiments with 60,000 randomized instances. J48, MLP and Bayes network algorithms were employed to model the classification problem. The J48 algorithm reported the best results with an accuracy of 93.1083% and true positive rate of 0.93. An in-depth comparative analysis between Bayes net, logistic regression, IBK, J48, PART, JRip, random tree, RePTree and random forest combined with a 10-fold cross-validation was also performed by [PERSON] et al. [15]. They reported a highest accuracy score of 91.523%. [PERSON] et al. [16] in their paper leveraged Apache Spark to employ four classifiers support vector machine, naive Bayes, decision tree and random forest. They experimented using the UNSW-NB15 dataset with all 42 features to detect intrusion. Evaluation metrics such as accuracy, sensitivity, specificity, training time and prediction time were used for comparing algorithms. Results indicated that random forest performed the best by achieving the highest accuracy score of 97.49%. In another research, a hidden naive Bayes classifier was used by [PERSON] et al. [17] for intrusion detection. It was compared with other classification algorithms. Moreover, the hidden naive Bayes classifier was found to perform better than the naive Bayes algorithm as it achieved an accuracy score of 93.72%. In their paper, [PERSON] et al. [18] proposed a deep neural network (DNN) model for intrusion detection. They used the NSL-KDD dataset for training the models. They selected only 6 out of 41 features. In this approach, they achieved an accuracy of 75.75%. Further, they compared the performance of this approach with another classification algorithm and found a random tree as the best performing algorithm with an accuracy 81.59%. [PERSON] et al. [19] proposed an enhanced feature selection technique named simulating annealing, then they used an SVM classifier for intrusion detection. They used the NDL-KDD'99 dataset to train the model. The results of the proposed method were  
Figure 1: High-level architecture of intrusion detection system (IDS). compared with previously presented results using GWO-SVM and PSO-SVM classifiers and gained 8.71% better accuracy and 43.64% lesser execution time. In their paper, [PERSON] et al. [20] proposed a model to detect network intrusions. Two famous heterogeneous datasets, UNSW NB-15 and UGR'16, were used for the model. A stack-based meta classification technique was used for classification. The results of the model show this approach as a good ensemble classification technique; they achieved an accuracy of 94% and 97.19% for the UNSW NB-15 and UGR'16 datasets, respectively. In [21], [PERSON] proposed a multiclass classification approach using support vector machine classifiers for intrusion detection and misuse detection. They used the KDD'19 dataset for their experiment. The work achieved 91.6738% of accuracy while performing a 23-class classification and the lowest cost for each test sample was 0.252854. They found SVM was the better performing algorithm for intrusion detection tasks in comparison with artificial neural networks. Pertaining to the studies that have been conducted in the past, in our research we aim to identify the following:  
1. Experiment with various ensemble feature selection techniques to identify the best set of features. 2. Build a baseline model to set a benchmark for future studies. 3. Analyse the performance of feature selection techniques based upon the time taken to select a particular set of features. This is particularly essential for building a low latency real-time system. In this paper, the authors present the major contributions of the work to build an accurate and fast intrusion detection system.