## 4 Experimental Results and Discussion
### Comparison Analysis of Results  
To verify the effectiveness of the proposed solution approaches, we compare our model with several published methods that used the same evaluation datasets in term of binary classification and multi-classification. Figure 11, along with Table 13, compares the performance of our proposed models for binary classification with other state-of-art models using IoTID20. The comparison reveals that our proposed approaches achieve higher performance results (accuracy, precision, recall, f1-measure) than previously published approaches performed with different feature selection strategies. The proposed techniques (IMF, UMF) achieve the highest classification accuracy with 99.98% compared to the published benchmarks. Moreover, the proposed model using both IMF and UMF achieves very high-performance precision, recall, and f1-measure. Moreover, we have verified the quality of the proposed model in comparison with other state-of-art models using the second dataset (i.e., NSL-KDD) in term of binary classification (detection model) as shown in Figure 12, along with Table 14. The proposed techniques (IMF, UMF) also achieved the highest classification accuracy with 99.79%. Hence, the proposed model is more efficient compared to the existing state-of-the-art models presented in previous works [53; 54; 55; 56] for the IoTID20 dataset and [42; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74] for the NSL-KDD dataset. This is attributed to the effectiveness of high dimensionality reduction by removing irrelevant features. Figure 13, along with Table 15, shows a performance comparison of our detection model with other state-of-the-art models using the NSL-KDD dataset. From Figure 13, along with Table 15, we observe that our proposed model has a very good accuracy result. From Figure 13, along with Table 15, we can observe that the Intersection (IMF) theory and union (UMF) theory provide the same detection accuracy 99.79%. Figure 10: Accuracy results yielded by the five various ML with different FS approaches for multi-classification using the IoTID20 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{**(A) G-ANN 60**} \\ \hline
**Parameters** & \multicolumn{3}{c}{**Miani**} & \multicolumn{3}{c}{**Dos**} & \multicolumn{3}{c}{**Scan**} & \multicolumn{3}{c}{**MAS**} & \multicolumn{3}{c}{**Normal**} & \multicolumn{3}{c}{**Parameters**} & \multicolumn{3}{c}{**Miani**} & \multicolumn{3}{c}{**Dos**} & \multicolumn{3}{c}{**Scan**} & \multicolumn{3}{c}{**MAS**} & \multicolumn{3}{c}{**Normal**} \\ \hline
**FP Rate** & 0.074 & 0 & 0.021 & 0.011 & 0 & **FP Rate** & 0.011 & 0 & 0 & 0 & 0 & **FP Rate** & 0.08 & 0 & 0 & 0.001 & 0 \\
**FF Rate** & 0.074 & 0 & 0.21 & 0.011 & 0 & **FP Rate** & 0.011 & 0 & 0 & 0 & 0 & **FP Rate** & 0.08 & 0 & 0 & 0.001 & 0 \\
**Precision** & 96.2 & 99.7 & 85.9 & 78.8 & 99.9 & Precision & 99.5 & 100 & 99.7 & 97.8 & 100 & Precision & 99.6 & 100 & 99.7 & 96.2 & 100 \\
**Recall** & 96.1 & 99.2 & 92.7 & 67.7 & 100 & **Recall** & 99.8 & 99.9 & 98.5 & 96.4 & 99.9 & Recall & 99.9 & 99.9 & 96.7 & 97.4 & 100 \\
**F-Measure** & 96.2 & 99.4 & 89.1 & 72.8 & 99.9 & **F-Measure** & 99.6 & 99.9 & 99.1 & 97.1 & 100 & F-Measure & 99.7 & 99.9 & 99.2 & 97.8 & 100 \\
**ROC Area** & 97.8 & 99.8 & 96.9 & 75 & 100 & ROC Area & 100 & 100 & 99.9 & 100 & 100 & ROC Area & 99.5 & 99.9 & 99.3 & 98.6 & 100 \\ Accuracy & 96.2 & 99.5 & 89.3 & 73.3 & 100 & Accuracy & 99.7 & 100 & 99.1 & 97.1 & 100 & Accuracy & 99.8 & 100 & 99.2 & 97.8 & 100 \\ \hline
**(B1C-C45.60)** & & & & & & & & & & & & & & & & \\
**F-Measure** & 99.4 & 99.9 & 98.5 & 93.9 & 100 & **F-Measure** & 99.9 & 99.7 & 96.7 & 99.1 & 100 & & & & & \\
**F-Measure** & 99.6 & 100 & 99.6 & 98.7 & 100 & ROC Area & 99 & 99.8 & 98.8 & 97.3 & 100 & & & & & \\
**Accuracy** & 99.4 & 99.9 & 98.5 & 93.9 & 100 & Accuracy & 99 & 99.7 & 96.7 & 99.1 & 100 & & & & & \\ \hline \end{tabular}
\end{table} 
Table 10: Summary of calculated statistical parameters for test IOTD20 dataset using CR with top-ranked 60 features.