## 6 Experimental Setup
### Experimental Runs of the Oversampling Methods  
The oversampling sub-experiment comprised five experimental runs (ERs) for each AMiDS type. We use the following notations to denote each experimental run: ER-KMS, ER-LRS, ER-ROS, ER-SMT, and ER-SVMSMT. ER stands for the experimental run and KMS, LRS, ROS, SMT, and SVMSMT stand for KMeanSMOTE, LoRAS, random oversampling, SMOTE, and SVM SMOTE, respectively. To distinguish between the PCA- and oSVM-based AMiDS, we use the subscripts \({}_{p}\) and \({}_{O}\); for example, ER-ROS\({}_{p}\) and ER-ROS\({}_{O}\). Before applying the oversampling methods, we normalized the PiB dataset's numerical features. Then, we transformed the categorical features using one-hot encoding before splitting the transformed dataset into the training and testing datasets. As a result, before sampling, the training dataset had 90 features and 1600 examples, whereas the testing dataset set had a similar number of features but 400 examples. Additionally, the testing dataset was independent of oversampling; however, the training data depended on oversampling. Consequently, the number of examples in the training dataset changed after sampling. In the training phase, the total number of examples after sampling using KMS, LRS, ROS, and SMOTE was 2992 with equally balanced normal and anomalous examples (i.e., 1496 each). The SVMSMT yielded 2161 examples but did not balance the normal and anomalous examples equally. As a result, the numbers of normal and anomalous examples were 665 and 1496, respectively. After sampling for the testing phase, the total number of samples was 2939 (1206 normal and 1187 anomalous) for all methods except the SVMSMT method, where the total number of samples was 1728 (529 normal and 1199 anomalous). The evaluation results after training and scoring (testing) the AMiDS models are shown in Tables 4-9 for the PCA-based AMiDS and Tables 10-15 for the oSVM-based AMiDS. Figure 7 shows the ROC curve and AUC values for the PCA-based AMiDS trained and scored models, and Figure 8 shows the ROC curve and AUC values for the oSVM-based AMiDS trained and scored models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-KMS\({}_{P}\) &... & 0.67 & 1.0 & 0.80 &... &... &... & 1496 \\ ER-LRS\({}_{P}\) &... & 0.97 & 0.64 & 0.77 &... &... &... & 1496 \\ ER-ROS\({}_{P}\) &... & 0.67 & 1.00 & 0.80 &... &... &... & 1496 \\ ER-SMIT\({}_{P}\) &... & 0.69 & 1.00 & 0.81 &... &... &... & 1496 \\ ER-SVMSMT\({}_{P}\) &... & 0.45 & 0.13 & 0.20 &... &... & 1496 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Trained PCA-based AMiDS evaluation results for the oversampling methods based on the anomalous case. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-KMS\({}_{P}\) & 0.97 & 0.97 & 0.97 & 0.97 & 0.75 &... & 0.2249 & 400 \\ ER-LRS\({}_{P}\) & 0.60 & 0.90 & 0.60 & 0.71 & 0.534 &... & 0.2090 & 400 \\ ER-ROS\({}_{P}\) & 0.97 & 0.97 & 0.97 & 0.75 &... & 0.1820 & 400 \\ ER-SMT\({}_{P}\) & 0.97 & 0.97 & 0.97 & 0.75 &... & 0.2347 & 400 \\ ER-SVMSMT\({}_{P}\) & 0.15 & 0.78 & 0.15 & 0.22 & 0.318 &... & 0.2133 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scored PCA-based AMiDS evaluation results for the oversampling methods based on the weighted average. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular}  & \ \begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular}  &