## 5 Experimentation and Result Analysis
### Result Analysis  
We conducted experiments on a modified NSL-KDD+ dataset balanced by the MC-GAN approach. In addition, we created the NSL-KDD+ and NSL-KDD+20 datasets that include all 41 features and 20 selected features. To validate the outcome of the proposed model, we utilized the datasets in training (80%) and testing (20%) samples. The proposed model uses the linear correlation feature selection approach to diminish the features and select significant characteristics for training purposes. In this work, we set 20 features that aid the model in learning from the selected low-dimensional parts to improve the classification outcome of the classifiers.  
Table 1 provides the class-wise outcome achieved by the proposed model on NSL-KDD+ and NSL-KDD+20 datasets. In the NSL-KDD+ dataset, the F1-score outcome for the typical class was 95.78%, with a DoS of 94.31%, probe of 84.87%, R2L of 94.57%, and U2R of 81.45%. The false-positive rate (FPR) outcome for the typical class was 4.57%, with a DoS of 0.87%, probe of 2.14%, R2L of 0.51%, and U2R of 0.69%. As we specified earlier in Section 5.2, FPR should be less to ensure that the proposed model achieved a better outcome. Further, we applied the proposed model on the selected 20 significant features termed NSL-KDD+20. In the NSL-KDD+20 dataset, the F1-score result for the typical class was 96.91%, with a DoS of 94.87%, probe of 85.74%, R2L of 95.71%, and U2R of 82.97%. The false-positive rate (FPR) outcome for the typical class was 4.14%, with a DoS of 0.74%, probe of 2.45%, R2L of 0.47%, and U2R of 0.71%. Based on the analysis of both datasets, we noticed that the proposed model on the NSL-KDD+20 dataset provides a better outcome than the NSL-KDD+ dataset. This outcome is achieved due to formal learning from samples by the proposed model. Figures 3-6 show the precision, recall, specificity, and F1-score of different classes on NSL-KDD+ and NSL-KDD+20 datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset with Features** & **Class** & **Precision** & **Recall** & **FPR** & **Specificity** & **F1-Score** \\ \hline \multirow{8}{*}{NSL-KDD+} & Normal & 92.04\% & 93.45\% & 4.57\% & 96.47\% & 95.78\% \\ \cline{2-6}  & DoS & 93.51\% & 92.74\% & 0.87\% & 97.54\% & 94.31\% \\ \cline{2-6}  & Probe & 91.78\% & 90.27\% & 2.14\% & 98.12\% & 84.87\% \\ \cline{2-6}  & R2L & 90.47\% & 94.12\% & 0.51\% & 95.97\% & 94.57\% \\ \cline{2-6}  & U2R & 88.54\% & 84.54\% & 0.69\% & 89.78\% & 81.45\% \\ \hline \multirow{8}{*}{NSL-KDD+20} & Normal & 93.80\% & 94.12\% & 4.14\% & 96.89\% & 96.91\% \\ \cline{2-6}  & DoS & 94.21\% & 91.54\% & 0.74\% & 98.57\% & 94.87\% \\ \cline{2-6}  & Probe & 92.54\% & 89.71\% & 2.45\% & 98.90\% & 85.74\% \\ \cline{2-6}  & R2L & 89.12\% & 93.74\% & 0.47\% & 94.78\% & 95.71\% \\ \cline{2-6}  & U2R & 86.87\% & 85.78\% & 0.71\% & 88.74\% & 82.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The outcome of the proposed model for multi-class classification.  
Figure 4: Recall rate of different classes on NSL-KDD+ and NSL-KDD+20 datasets.  
Figure 3: Precision rate of different classes on NSL-KDD+ and NSL-KDD+20 datasets.