## 4 Experimental Results and Discussion
### Comparison Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** \\ \hline
[53] & SVM & 40 & 55 & 37 & 16 \\
[53] & Ensemble & 87 & 87 & 87 & 87 \\
[54] & SVM & 97 & - & - & - \\
[54] & Ensemble & 94 & - & - & - \\
[54] & SLFN & 98 & - & - & - \\
[56] & XGBost & 99.79 & - & 98 & 1 \\
[56] & SVM & 98.76 & - & 98.00 & 98.00 \\
**Proposed** & UMF & 99.98 & 99.90 & 99.90 & 99.90 \\ \hline
**Proposed** & IMF & 99.98 & 99.90 & 99.90 & 99.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The comparison results of our detection model performance and related works of IoTID20. Figure 12: The comparison of accuracy result of our detection model performance and related works of NSI-KDD. Based on the obtained results of these experiments summarized in the tables and figures above, accuracy performance increased via reducing feature dimensionality using IMF-Ensemble and UMF-Ensemble as compared to using only the classification via the IG or GR approach. Furthermore, independent (individual) classifiers achieved better performance than the UMF-Ensemble and UMF-Ensemble. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Model** & **Accuracy (\%)** \\ \hline
[54] & SLFN-SVM-SMOTE & 93.5 \\
[54] & SLFN & 98.4 \\
[75] & SMOTE + LSTM & 86.2 \\
[75] & LSTM & 79.5 \\
[75] & kNN & 86.5 \\
[53] & Ensemble & 87 \\
[76] & Autoencoder & 94.7 \\
[77] & RF & 97.9 \\
[78] & ensemble & 98.7 \\
**Proposed** & UMF & 99.7 \\
**Proposed** & IMF & 99.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The comparison results of our multi classification model performance and related works of IoTID20 dataset. Figure 13: The comparison results of our multi classification model performance and related works of IoTID20 dataset. ter classification accuracy using optimum top-ranked features (IMF, UMF) compared to features selection obtained by the IG and GR approaches. For instance, the Bagging classifier achieved the highest accuracy (%99.91) by using the UMF approach (28 optimum top-ranked features) compared to the various numbers of selected features. In the ANN classifier, the accuracy classification fluctuates up and down by applying various feature selection approaches. There is a noticeable increase in the accuracy performance of the ensemble classifier when compared to individual classifiers. In terms of selected features, a small number of relevant features are not required to achieve the higher accuracy whereas many relevant features do not assure that a lower classification accuracy will result. Based on the obtained results from the IoTID20 dataset, the proposed UMF and IMF results in the improvement of Anomaly-based IDS for accuracy, precision, recall, f1-measure, ROC area of 99.98, 99.90, 99.90, 99.90, 99.90, respectively. The improvement in the performance of IMF-ensemble or UMF-ensemble is because IMF and UMF filter-based approaches use an optimum (min, max) number of top-ranked features relevant for classification. UMF and IMF do not show any fluctuation in the performance since UMF, and IMF incorporates the best features produced by both IG and GR approaches. Furthermore, the model with UMF and IMF is not prone to underfitting when the number of features decrease. According to the results shown in Table 13, the proposed model is superior and competent in comparison to the results stated for models reported in [53; 54; 55; 56] using IoTId20 dataset. Furthermore, we used the NSL-KDD dataset for validating our binary classification detection model. Table 14 indicates that the proposed model is superior and competent in comparison to the results stated for the models reported in [42; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74]. Figure 13, along with Table 15 represent a comparison between our multi classification model performance versus the related works with respect to the IoTID20 dataset.