## 2 Related Works
### Resampling Algorithms  
[PERSON] et al. [4] presented a novel approach to improving the performance of IDSs by combining SMOTE with a sequential classifier method. SMOTE, a prevalent oversampling technique used to address imbalanced datasets, alleviates the scarcity of samples in minority classes by generating synthetic instances [5]. This method identifies neighboring examples in the feature space, establishes connections between them, and subsequently introduces new samples along this line. It is crucial to underscore that this process does not involve a literal increase in cyber-attacks; instead, it strategically enriches the dataset with synthetic instances that represent minority class patterns. Through this strategic synthesis of data points within the feature space, SMOTE plays a pivotal role in redressing class imbalances, ultimately enabling the creation of robust machine learning models for attack detection. [PERSON] et al. proposed a methodology that utilizes SMOTE to address class imbalance in the IDS dataset and enhance the accuracy of intrusion detection. By integrating SMOTE with sequential classifiers on the NSL-KDD dataset of Network IDS (NIDS), the study demonstrated improved sensitivity (0.95) and accuracy (0.93) in detecting intrusions. [PERSON] et al. [6] conducted a study to examine the impact of feature selection, SMOTE, and undersampling methods on class imbalance classification. The authors explored various techniques to address imbalanced datasets in classification tasks, evaluating their performance on the KDD CUP 99 IDS dataset using metrics such as true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The study provided insights into the effectiveness of feature selection and sampling techniques in enhancing classification accuracy and handling class imbalance across different domains. The incorporation of SMOTE into specific machine learning algorithms resulted in noteworthy enhancements, exemplified by the random forest (RF) exhibiting a true positive rate of 1.0 for normal activities, 0.998 for DOS, 0.937 for probe, 0.975 for R2L, and 0.673 for U2R attacks. However, it is important to note that direct comparisons with our method are not feasible due to the dissimilarities in datasets and the absence of comprehensive classification metrics in their evaluation. [PERSON] et al. [7] applied feature selection and RF with SMOTE on the NSL-KDD dataset for intrusion detection. Random forest is a machine learning algorithm that combines multiple decision trees to make predictions. In their method, [PERSON] et al. utilized feature reduction techniques to select the most relevant features from the dataset. SMOTE was employed as an oversampling technique to address the class imbalance in the dataset by generating synthetic samples from the minority class. The implementation was conducted on a 2.4 GHz Intel Core i5-2430M processor with 4 GB RAM, utilizing the WEKA machine learning tool version 3.6.9. The programming language used in the study are not mentioned in the information provided. To evaluate the performance of their approach, [PERSON] et al. used recall, false positive rate (FPR), and precision metrics on the NSL-KDD IDS dataset. They partitioned the 22 attacks in the NSL-KDD dataset into five classes (Normal, DoS attack, Probing attack, R2L attack, and U2R attack) and evaluated the performance metrics for each group rather than individual attacks. Recall measures the ability of the model to identify true positive instances, FPR measures the rate of falsely classified negative instances as positive, and precision quantifies the proportion of correctly classified positive instances. The authors reported achieving a precision of 0.99 and an FPR of 0, indicating a high level of accuracy in identifying positive instances and a low rate of false positives. [PERSON] et al. [8] introduced the variational generative model (VGM) for intrusion detection, utilizing a variational autoencoder, a neural network capable of generating new data samples.