## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Processing** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline \multirow{4}{*}{RF} & Original & 78.43 & 78.83 & 84.28 & 81.37 & 78.16 \\  & MI & 97.10 & 98.0 & 98.0 & 98.0 & 97.60 \\  & MI-RFE & 97.30 & 98.51 & 98.11 & 98.30 & 98.20 \\ \hline \multirow{4}{*}{SVC} & Original & 78.35 & 77.41 & 84.28 & 80.50 & 77.90 \\  & MI & 89.33 & 91.70 & 90.60 & 90.20 & 89.0 \\  & MI-RFE & 96.50 & 97.20 & 97.0 & 97.0 & 96.50 \\ \hline \multirow{4}{*}{Bagging} & Original & 84.51 & 81.99 & 88.57 & 84.91 & 84.16 \\  & MI & 96.37 & 97.12 & 97.0 & 97.0 & 96.50 \\  & MI-RFE & 97.10 & 97.40 & 97.50 & 97.52 & 97.20 \\ \hline \multirow{4}{*}{DT} & Original & 84.31 & 83.90 & 88.57 & 85.84 & 84.0 \\  & MI & 96.0 & 96.62 & 96.50 & 96.49 & 95.97 \\ \cline{1-1}  & MI-RFE & 97.12 & 97.50 & 97.40 & 97.30 & 97.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the experimental method for a multi-SCADI dataset using 10-fold cross-validation. Determining feature importance is an important strategy for understanding the ML model as well as the features of the underlying data. The Shapley value explanation (SHAP) is a method for properly evaluating the relevance of input features in a model output. Figure 2 depicts the most relevant features for both binary and multi-class dataset after employing MI-RFE feature selection methods based on the RF model. As shown in Table 6, we compared the effectiveness of our proposed approach to previous studies that employed the same SCADI dataset. Table 6 contains some abbreviations such as FS (feature selection), NF (number of features), and ACC (accuracy). A comparative evaluation of the proposed approach with relevant previous studies is summarized in Table 6. Overall, the proposed method improved all previous research results on the SCADI dataset, including: ANN [15], KNN [16], FNN [18], hybrid autoencoder [20], RF [21], and AdaBoost [22]. Comparing the results of other relevant work, our proposed model has the best performance by obtaining an accuracy of 98.60% and 99.10% for binary and multi-class datasets, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline \multirow{4}{*}{Binary} & RF & 99.10 & 98.50 & 99.80 & 99.17 & 98.60 \\  & SVC & 95.55 & 91.52 & 99.90 & 95.57 & 90.77 \\  & Bagging & 98.0 & 96.43 & 99.20 & 98.10 & 96.30 \\  & DT & 98.20 & 96.45 & 99.81 & 98.20 & 98.30 \\ \hline \multirow{4}{*}{Multi} & RF & 98.60 & 98.70 & 99.80 & 98.59 & 98.60 \\  & SVC & 97.55 & 98.21 & 99.90 & 95.57 & 90.77 \\ \cline{1-1}  & Bagging & 98.30 & 98.55 & 98.20 & 98.60 & 98.27 \\ \cline{1-1}  & DT & 98.12 & 98.05 & 98.01 & 98.10 & 97.70 \\ \hline \end{tabular}
\end{table} 
Table 5: Experimental results for the SCADI datasets based on the optimal hyperparameters. Figure 2: Feature importance for SCADI datasets. Compared to those in the literature, the model used shows high prediction in accuracy, and the reason for this is selecting the most important features during the training process and reducing data noise by using hyper feature selection model based on optimal hyperparameters.