## 3 Related Work  
Imbalanced datasets and improper data segmentation are the main contributors to a loss of the IDS detection accuracy. The authors of [34] proposed a machine learning framework in which they combined a variational autoencoder (VAE) and multilayer perceptron to simultaneously tackle the issues of imbalanced datasets (HDFS and TTP datasets) from multiple data sources and intrusions in complex, heterogeneous network environments. Using a hybrid learning approach, the authors used the variational autoencoder to address the imbalanced datasets issue in the training stage. However, they integrated the encoder from the unsupervised model into the multilayer perceptron to transfer imbalanced learning to the classifier model. They used supervised learning to accurately classify abnormal and normal examples. In addition, the authors had to balance the system's performance and complexity due to using the multilayer perceptron for classification. In contrast, our non-hybrid approach uses supervised learning, simple data-level imbalanced learning methods, and an IoT imbalanced dataset of a relatively small size (2000 examples) to enable imbalanced learning while classifying normal and abnormal examples. The proposed hybrid approach achieved better results than other solutions regarding the F1-score and recall rate, however, it exhibited substantial training and testing times. Generally, generative models, such as autoencoders, are more effective for solving the data imbalance problem than non-generative or traditional synthetic sampling methods when dealing with high-dimensional data. To that extent, the authors of [35] proposed a variational autoencoder method that generated new samples similar to, but not the same as, the original dataset by learning the data distribution. Given various evaluation metrics, the generative model outperformed the traditional sampling methods, such as SMOTE, ADASYN, and borderline SMOTE. Unlike the hybrid VAE method, which focuses on the sampling of network data, the non-hybrid VAE method considers an image dataset. This study uses non-hybrid generative methods to sample network data. While the authors of [34] used a non-traditional approach to solve the data imbalance problem, the authors of [36] used traditional resampling methods (random undersampling, random oversampling, SMOTE, and ADA-SYN) to investigate the influences of these methods on the performance of artificial neural network (ANN) multi-class classifiers using benchmark cybersecurity datasets, including KDD99 and UNSW-NB18. Comparing the performance of the ANN multi-class classifiers using the resampling techniques, their study revealed that undersampling performed better than oversampling in terms of the training time, and oversampling performed better in terms of detecting minority data (abnormal examples). However, both methods significantly increased the recall when the dataset was highly imbalanced. Furthermore, when comparing oversampling, undersampling, and SMOTE in terms of the resampling time, the authors found that oversampling and undersampling had the shortest time requirements, and the SMOTE resampling time was average. However, the ADASYN resampling time was the longest. In addition to the Random Oversampler and SMOTE methods, our study investigates the influence of other oversampling methods, such as KVSMOTE and KMeanSMOTE, hybrid methods such as SMOTEEN and SMOTETomek, ensemble methods such as EasyEnsemble and BalanceCascade, localized random affine shadow sampling known as LORAS, and generative methods such as autoencoders and variational autoencoders on the performance of two AMiDS models designed using PCA and one-class SVM (oSVM) and trained using a binary imbalanced IoT dataset exhibiting rare event cases, i.e., the number of abnormal examples (1874 or 93.7%) is proportionally higher than the number of normal examples (126 or 6.3%) in the dataset.