## 3 Related Work
Similar to [36], the authors of [3] considered resampling methods with optimization techniques to address issues resulting from imbalanced or inadequate datasets, such as overfitting or poor classification rates. However, in addition to resampling methods, the authors of [3] used hyperparameter tuning (HyPaT) to increase the classifier's sensitivity. In particular, using SMOTE, the authors proposed a combination of the oversampling, undersampling, and K-Nearest Neighbor methods with grid-search (GS) optimization andHyPaT to improve the classification results of different supervised learning algorithms, including SVM, among other supervised classifiers. Applying SMOTE with GS and HyPaT improved the results of the classifiers, while our study uses SMOTE, among other methods, to address the data imbalance by applying the random-search optimization method to reduce the computational overhead. Using the KDD and NSL-KDD datasets with imbalanced examples, a deep learning intrusion detection system could not detect training samples in the minority classes. To handle this data imbalance issue, the authors of [37] proposed an IDS based on a Siamese neural network to detect intrusions without using traditional data sampling techniques, such as oversampling and undersampling. The proposed IDS achieved a higher recall rate for the minority classes of the attack than other neural-network-based IDSs trained using the imbalanced datasets. The author of [38] evaluated the effect of a class imbalance on the NSL_KDD intrusion dataset using four classification methods. To address the data imbalance in network intrusion datasets, the authors of [39] used a combination of undersampling and oversampling techniques and clustering-based oversampling to evaluate the effect of imbalance on two intrusion datasets constructed from actual Notre Dame traffic. An analysis of the receiver operating characteristic (ROC) curves revealed that oversampling by synthetic generation outperformed oversampling by replication. To examine the influence of resampling methods on the performance of the multi-class neural network classifier, the authors of [36] used several data-level resampling methods on cybersecurity datasets. Using precision, recall, and the F1-score to evaluate the results, the authors found that oversampling increased the training time, while undersampling decreased the training time. In addition, resampling had a minor impact on the extreme data imbalance, and oversampling, in particular, detected more of the minority data. The study presented in [40] considered the use of ensemble-based approaches to address the data imbalance problem for intrusion detection in industrial control networks using power grid datasets. The authors compared nine single and ensemble models specifically designed to handle imbalanced datasets, such as synthetic minority oversampling bagging and synthetic minority oversampling boosting. They evaluated their performance using balanced accuracy, Kappa statistics, and AUC metrics. Additionally, the authors listed the average training and testing times for each evaluated method or algorithm measured in seconds. The study revealed that undersampling and oversampling strategies were affected when using boosting-based ensembles. Our study uses ensemble-based methods not for classification purposes but rather for data sampling. For the classification task, we use PCA and oSVM.