## 2 Related Works  
[PERSON] et al. [20] proposed an intrusion detection system for the UNSW-NB15 dataset. They performed feature selection in two stages. In the first stage, they computed the best features using information gain and Random Forest methods and selected the common features received from these algorithms. In the second stage, they gave these features to the RFE algorithm to eliminate the low-ranked features. For the classification, they used a multi-layer perceptron (MLP) classifier. They used only one classifier; however, experimentation with various classifiers could have potentially yielded improved classification metrics for this research. [PERSON] et al. [21] used UNSW-NB15 and KDD-Cup99 datasets for experiments. They reduced the input features using the PCA algorithm. For classification, they used SVC with four different kernels, namely, linear, polynomial, Gaussian radial bias, and sigmoid. They achieved the best classification metrics when the Gaussian radial bias kernel was used. It is important to highlight that SVC is perceived as a slow algorithm since it works to find the optimal separating hyperplane that maximizes the margin between classes while minimizing classification errors. The work conducted by [PERSON] and [PERSON] [9] employed UNSW-NB15 and NSL-KDD datasets in their research. They used a stacked autoencoder for dimensionality reduction. Next, they developed a bi-directional long short-term memory model for classification. They improved this classifier by implementing an attention mechanism that modifies the weights to give more importance to key features. As a result, they achieved better results than traditional models. [PERSON] [22] proposed an intrusion detection system for industrial IoT networks. This researcher used the UNSW-NB15 dataset in his work. Feature selection is performed using a Genetic Algorithm where a Random Forest is used as a fitness function. Random Forest, Logistic Regression, Naive Bayes, Decision Tree, Extra-Trees (ET), and Extreme Gradient Boosting (XGB) classifiers are used for binary classification. This research achieved the best classification accuracy when using the RF classifier with 16 selected features. This research did employ different classifiers but did not leverage an ensemble model. [PERSON] et al. [10] performed their experiments on the UNSW-NB15 dataset. Their dimensionality reduction method performs feature extraction without compromising the representation of rarely occurring labels. They accomplished this task by designing an encoder-decoder neural network associated with a specialized module that ensures extracted features have the power to represent rare labels. Next, they input these refined features to the estimator to perform classification. Their results show improvement in accuracy and reduction in false positive rate. An issue with the autoencoder is that it suffers from overfitting if the number of nodes increases [23]. The intrusion detection approach proposed by [PERSON] et al. [24] used the UNSW-NB15 dataset for training and real-time data for testing the proposed system. Feature selection is performed using the information gain technique, while classification is performed using C5, the Chi-square Automatic Interaction Detector (CHAID), Classification Regression Trees (CART), and the Quaternion Estimation (QUEST) algorithms. This research achieved better classification performance as compared to tree-based models. This system is designed to detect only five attack categories present in the dataset. However, a total of nine attack categories are present in the dataset. [PERSON] and [PERSON] [25] used the UNSW-NB15 dataset in their research. They pruned the features using a filter-based feature selection technique combined with the XGBoost algorithm. They evaluated the predictive performance of reduced features over Support Vector Machine (SVM), K-Nearest Neighbor, Logistic Regression, Artificial Neural Network (ANN), and Decision Tree classifiers.