## 5 Discussion of Experimental Results  
### First-Stage Experimental Results  
This section presents the experimental results of the first stage of the proposed two-stage fine-tuning algorithm. In this stage, we applied the Gaussian noise, WGAN-GP, and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Quantity** & **Percentage** \\ \hline
0 & Normal & 67,343 & 51.42\% \\
1 & Probe & 10,656 & 8.90\% \\
2 & DoS & 45,927 & 35.06\% \\
3 & U2R & 52 & 0.04\% \\
4 & R2L & 5995 & 4.58\% \\ \hline  & All & 130,973 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Class distribution of NSL-KDD after R2L data enhancement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Quantity** & **Percentage** \\ \hline
0 & Normal & 67,343 & 51.42\% \\
1 & Probe & 16,656 & 12.72\% \\
2 & DoS & 45,927 & 35.06\% \\
3 & U2R & 52 & 0.04\% \\
4 & R2L & 995 & 0.76\% \\ \hline  & All & 130,973 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class distribution of NSL-KDD after Probe data enhancement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Quantity** & **Percentage** \\ \hline
0 & Normal & 67,343 & 51.42\% \\
1 & Probe & 16,656 & 12.72\% \\
2 & DoS & 45,927 & 35.06\% \\
3 & U2R & 52 & 0.04\% \\
4 & R2L & 995 & 0.76\% \\ \hline  & All & 130,973 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Class distribution of NSL-KDD after U2R data enhancement. SMOTE data augmentation techniques to enhance the sparse data classes Probe, U2R, and R2L in the NSL-KDD dataset, respectively. The augmented data were then combined with the original dataset for retraining and classification. As shown in the following six tables, the generated sparse data classes such as Probe, U2R, and R2L included the enhanced data of each attack class and were merged into the original training dataset. Tables 7 and 8 show the results of our experiments conducted using machine learning algorithms and neural network models to identify the Gaussian noise data enhancement, respectively. Tables 9 and 10 show the experimental results of identifying WGAN-GP data enhancement by using machine learning algorithms and neural network models, respectively. Tables 11 and 12 show the results of our experiments conducted using machine learning algorithms and neural network models to identify the SMOTE data enhancement, respectively  
Tables 7-12 revealed that the data enhancement experiments of the Probe attack class achieved good results on the NSL-KDD dataset, irrespective of whether the Gaussian noise, WGAN-GP, or SMOTE methods were used to enhance the data. Most of the classifiers could effectively improve the recognition accuracy of the Probe class. For the other two rare attack classes, U2R and R2L, although most of the data enhancement techniques could also improve the recognition accuracy of these two attack classes, the accuracy rate was still very low. Observing these two types of experimental data revealed that for all algorithmic classifiers, even when data augmentation was used to improve the sparse data problem, these two types of attacks were difficult to identify effectively. In our opinion, the possible reason was that the percentage of this enhanced class was still very low as compared to the percentages of other classes. When the percentage of a certain type of data distribution was very low, it could easily be considered noise and could not be identified by machine learning and neural network models. In fact, for the U2R or R2U class, the augmented 5000 data items might be distorted, and the percentages of these two data distributions were still very low as compared to the other classes to improve the recognition accuracies of these two data types. Due to the extreme paucity of information on these two types of attacks, U2R and R2L, it is difficult to make significant improvements using different data enhancement techniques. Priority should be given to increasing the collection of real data on this type of data.