## 2 Related Works
### Resampling Algorithms
To handle class imbalance in the NSL-KDD dataset, the authors incorporated seven variants of SMOTE and ADASYN (adaptive synthetic sampling). They evaluated the performance of the VGM using accuracy and F1 score as metrics. The VGM achieved an average accuracy of 0.7685, indicating a relatively high level of correct predictions, while the average F1 score was reported as 0.7410, demonstrating a good balance between precision and recall in the model's performance. The specific programming language used in their study was not specified. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed SMOTE_NCL, an innovative approach that combines SMOTE with neighborhood cleaning rule (NCL) for resampling the KDD CUP 99 IDS dataset. SMOTE_NCL aims to address class imbalance by generating synthetic samples using SMOTE and then applying NCL to remove noisy and boundary data points. The NCL employs the KNN algorithm to identify and remove noisy samples and borderline instances from a dataset. Although [PERSON] and [PERSON] demonstrated improved area under the curve (AUC) (0.8060%) on the KDD CUP 99 IDS dataset with their algorithm, they acknowledged its limitations in terms of robustness and effectiveness. The evaluation of the algorithm's performance was based on the AUC parameter, which provides an overall assessment of the classifier's ability to discriminate between positive and negative instances. [PERSON], [PERSON], and [PERSON] et al. (2018) aimed to enhance the performance of machine-learning-based IDS by utilizing an imbalanced and up-to-date dataset. They employed various techniques, including SMOTE, Tomek links (it provides a way to identify and address overlapping instances in a dataset, aiding in the improvement of classification performance and the handling of class imbalance), and RF classifier to address class imbalance and improve the classification accuracy. The programming language used in their study is not specified. They evaluated the performance using several metrics, including accuracy, precision, recall, and F1 score, on the imbalanced dataset. [PERSON] et al. reported significant improvements in the performance of the IDS, achieving an accuracy of 98.44%, precision of 98.42%, recall of 97.23%, and F1 score of 97.82%. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) introduced a local adaptive composite minority sampling algorithm (LA-SMOTE) based on the deep learning gated recurrent unit (GRU) neural network. Their approach aims to address class imbalance by utilizing KNN to select low-frequency samples. Subsequently, high-frequency samples are chosen from the selected KNN and assigned to different regions of the sample space. The GRU neural network is then employed for training the classification model. The paper focused on building a combined intrusion detection model using imbalanced learning techniques and GRU. They evaluated the performance of LA-SMOTE on the NSL-KDD dataset and considered metrics such as FPR, false alarm rate (FAR), and accuracy to assess the effectiveness of their approach. The authors reported an average detection rate of 98.904, accuracy of 99.04, and FAR of 0.134, indicating the high performance of their model in accurately detecting intrusions. However, the programming language used for implementation is not specified in the paper. [PERSON] et al. [PERSON] et al. (2018) applied SMOTE in conjunction with various machine learning models to develop an IDS for vehicular ad hoc networks (VANETs) based on the IoN-IoT dataset. The aim of their study was to evaluate the performance of different machine learning algorithms and determine the most effective one. They implemented their method using Python version 3.8 programming language. The authors evaluated the performance of the IDS using metrics such as accuracy, precision, recall, F1 score, and FPR. The IoN-IoT dataset was derived from a large-scale, heterogeneous IoT network specifically designed for VANETs. [PERSON] et al.