## 6 Experimental Setup
### Experimental Runs of the Ensemble-Based Methods  
Like the hybrid-based methods, we conducted two experiments with four experimental runs to train and evaluate the PCA- and oSVM-based AMiDS models using two data sampler ensemble-based methods, i.e., EasyEnsemble and BalanceCascade. We applied the EasyEnsemble method first, followed by the BalanceCascade. Then, using notations consistent with those used in the previous runs, we named the experimental runs using the following notations ER-EENSMB\({}_{P}\), ER-BALCAS\({}_{P}\), ER-EENSMB\({}_{O}\), and ER-BALCAS\({}_{O}\), where EENSMB and BALCAS stand for the EasyEnsemble and BalanceCascade, respectively. Similar to the hybrid methods, we applied the ensemble-based methods after transforming the PiB data. The size of the resulting balanced data for the PCA-based AMiDS model, after applying the EENSMB and BALCAS methods, was 208 examples for both methods. However, for the oSVM-based model, the size of the resulting PiB data was 166 examples for both methods. Given the training and scoring phases, Tables 22-27 show the evaluation results for the two AMiDS models. Figure 10 shows the ROC curves and AUC values. \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-EENSMB\({}_{p}\) &... & 0.12 & 0.50 & 0.19 &... &... &... & 22 \\ ER-BALCAS\({}_{p}\) &... & 0.12 & 0.50 & 0.19 &... &... &... & 22 \\ ER-BALCAS\({}_{p}\) &... & 0.03 & 0.32 & 0.05 &... &... &... & 22 \\ ER-BALCAS\({}_{\mathcal{O}}\) &... & 0.10 & 0.27 & 0.14 &... &... &... & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 27: Scored PCA- and oSVM-based AMiDS evaluation results for the ensemble-based 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-EENSMB\({}_{p}\) &... & 0.65 & 0.51 & 0.57 &... &... &... & 104 \\ ER-BALCAS\({}_{p}\) &... & 0.65 & 0.51 & 0.57 &... &... &... & 104 \\ ER-EENSMB\({}_{\mathcal{O}}\) &... & 0.38 & 0.38 &... &... &... & 85 \\ ER-BALCAS\({}_{\mathcal{O}}\) &... & 0.69 & 0.39 & 0.50 &... &... &... & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Trained PCA- and oSVM-based AMiDS evaluation results for the ensemble-based methods based on the normal 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-EENSMB\({}_{p}\) &... & 0.60 & 0.72 & 0.65 &... &... &... & 104 \\ ER-BALCAS\({}_{p}\) &... & 0.60 & 0.73 & 0.66 &... &... &... & 104 \\ ER-EENSMB\({}_{\mathcal{O}}\) &... & 0.35 & 0.35 & 0.35 &... &... &... & 81 \\ ER-BALCAS\({}_{\mathcal{O}}\) &... & 0.56 & 0.81 & 0.66 &... &... &... & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Trained PCA- and oSVM-based AMiDS evaluation results for the ensemble-based methods based on the anomalous case.