## 2 Overview of SMOTE Schemes and Ensemble Learning Schemes
### RF Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Contributions and Experimental Results**} \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] (2002) [18] & \(\bullet\) & Synthetic minority oversampling technique (SMOTE), an oversampling method for balancing imbalanced data sets, is used to improve the accuracy of classifiers for a minority class. & \(\bullet\) & The SMOTE classifier outperformed an under-classifier, loss ratio-based classifier, and naive Bayes classifier. \\ \hline [PERSON] and [PERSON] (2013) & \(\bullet\) & This study investigated SMOTE from theoretical and empirical perspectives using simulated and empirical & \(\bullet\) & SMOTE improved the performance of k-NN classifiers for high-dimensional data when the number of variables was reduced using a variable selection method. \\ \hline [PERSON], [PERSON], [PERSON] (2018) & \(\bullet\) & A two-stage classifier approach for network intrusion detection systems (NIDSs) is applied to imbalanced data sets on intrusion detection; in this approach, minority and majority intrusion classes are separated in training and detection. & Minority and majority intrusion classes are separated in training and detection to improve the overall detection rate of minority classes and to reduce the error rate for UNSW-NB15 data set. \\ \hline [PERSON], [PERSON], [PERSON] (2019) & \(\bullet\) & A rough random forest (RF) algorithm in conjunction with binarization techniques is used to decompose an original data set into subsets of binary classes to balance multiclass imbalanced data sets. & The proposed method outperformed other methods featuring the Tree Bag Model and SMOTE+Tree Bag Model with respect to the receiver operating characteristic curve and corresponding area under the curve. \\ \hline [PERSON] et al. (2019) [21] & \(\bullet\) & Proposes a method of using the SMOTE to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. & \(\bullet\) & The simulations are conducted on a benchmark intrusion dataset KDDCup99, and the accuracy of the random forest algorithm has reached 92.39\%, which is higher than other comparison algorithms. \\ \hline [PERSON], [PERSON], [PERSON] (2020) [19] & \(\bullet\) & This stud proposed six machine -learning-based intrusion detection systems with SMOTE for data classification in conjunction with k-nearest neighbor (k-NN), RF, gradient boosting, adaboost, DT, and linear discriminant analysis algorithms. & \(\bullet\) & In experiments, the proposed approach considerably increased the detection rate for rarely encountered intrusions in the CSE-CIC-IDS 2018 data set. \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] (2020) & \(\bullet\) & Conduct the comparative experiments on analysis for the intrusion detection problems using Xgboost, Random Forest, Bagging, and Adaboost. & \(\bullet\) & Experimental results demonstrate that PSO-Xgboost model outperforms other comparative models in precision, recall, macro-average, and mean average precision (mAP) on NSL-KDD dataset. \\ \hline [PERSON], [PERSON], [PERSON] (2020) & \(\bullet\) & Combine the spatial feature and temporal feature, we fuse GBDT model and Gated Recurrent Unit (GRU) model to make a quadratic ensemble model as intrusion detection system. & \(\bullet\) & The experimental results show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods on CIC-IDS-2017 dataset. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SMOTE with machine learning approaches for network threat classification. Figure 4: RF for the number of trees \(n=100\) in our study. Figure 3: RF algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Features** & **Contributions and Experimental Results** \\ \hline \multirow{8}{*}{[PERSON], [PERSON].