## 4 Data-Level Sampling and Resampling Methods
Although this study focuses on rare events where normal events are less frequent, we consider the generative methods to train the AMiDS model to examine the ability of the trained AMiDS to classify unseen or new anomalous events. Table 1 briefly describes each method, and Table 2 classifies various data sampling techniques based on their approach. Figure 4: A block diagram of an autoencoder model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Description** & **References** \\ \hline Autoencoder (AE) & A deep learning method used to generate synthetic data using unsupervised learning. It consists of an encoder and a decoder. The encoder compresses the input data into a non-regularized latent space, and the decoder learns the latent space distribution to reconstruct the data such that they are closer to the original but not exactly the same. However, in our implementation, we use L1 regularization to enable the autoencoder to learn any important features in the latent space and to avoid overfitting. & [49] \\ \hline BalanceCascade & Similar to EasyEnsemble, except it undersamples the majority class by deleting examples using supervised deletion rather than random deletion. & [50, 51] \\ \hline EasyEnsemble & Uses an undersampling approach efficiently to randomly generate several subsets from the majority class examples without ignoring useful information. It then combines each generated subset with the minority class samples, where the size of the subset is similar to the size of the minority class examples to train an adaboost ensemble. & [50, 51] \\ \hline KMeanSMOTE & Combines k-means clustering and SMOTE oversampling to balance the data while avoiding unnecessary noise generation. & [52] \\ \hline LoRAS & Generates Gaussian noise in small neighborhoods around the minority class examples and uses multiple noisy data points to create the synthesized data. & [53] \\ \hline Random & Selects random examples from the minority class and then uses replacement to add the & \\ Oversampler (ROS) & selected examples to the dataset, thus providing duplicate instances from the minority & [2, 54] \\ \hline SMOTE & Resamples the minority class by creating synthetic examples instead of replacing them. & [55] \\ \hline SMOTE & Applies oversampling using SMOTE followed by undersampling using the Edited & \\ SMOTEEN & Nearest Neighbor (ENN) to provide in-depth data cleaning. It removes examples from & [56] \\  & both the minority and majority classes, performing better when the dataset contains a & \\ \hline SMOTETomek & Applies oversampling using SMOTE followed by Tomek links to clean the data by & \\ SMOTETomek & removing fewer examples from both the minority and majority classes compared to the & \\ SMOTETEND. However, similar to SMOTEN, it performs better on datasets with & \\  & a small number of positive examples. & \\ \hline SVMSMOTE & Applies SVM to find the borderline area and SMOTE oversampling to randomly & \\  & generate artificial data along the obtained borderline, thus establishing a clear boundary & [2] \\  & between the majority and minority classes. & \\ \hline Variational & Structurally similar to an AE, except that the input, latent representation, and output are & \\ Autoencoder (VAE) & probabilistic random variables. In addition, the encoder compresses the data into a & \\  & regularized latent space, while the decoder takes the latent representation as an input & \\  & and outputs a probability distribution of the data. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of data-level methods for imbalanced learning.