## 5 Discussion of Experimental Results
### Second-Stage Experimental Results
#### 5.2.3 Analysis of Optimal Number of Model Layers  
Based on the above results of fine-tuning the numbers of feature values and data augmentations, we further discuss the effect of fine-tuning the layer design of the MLP and CNN classifiers on the Probe recognition accuracy. Because of the different structures of the MLP and CNN models, the experiments at this stage were mainly aimed at the design of changing the dense depth of MLP and the convolutional depth of CNN. The experimental results are presented in Figure 11a, which shows the effect of changing the number of dense layers of the MLP classifier on the Probe recognition accuracy. In contrast, the experiment shown in Figure 11b analyzed the effect of the convolutional layers of the CNN classifier on the probe recognition accuracy.  
The experimental results of Figure 11 show that the model design with different depths of the neural network models MLP and CNN had a significant impact on the accuracy of Probe recognition. As shown in Figure 11a, when the number of dense layers used in MLP was four, the accuracy of Probe recognition could be improved up to the maximum of 80%. As shown in Figure 11b, when the CNN algorithm was designed with six convolution layers, the Probe recognition accuracy was improved to a maximum of 73%. Moreover, we found that the accuracy of Probe recognition decreased when the number of layers in both of the models became very large. This indicated that both of the classification models could easily cause the overfitting phenomenon if the design was too complicated for the NSL-KDD dataset.  
The results of the optimal model depth and the Probe recognition accuracy analysis for the neural network models MLP and CNN are shown in Table 16. This table shows that the accuracy of the Probe attack recognition could be further improved by fine-tuning the depth of the model for different neural network models by means of two-stage fine-tuning. For the MLP classifier in Table 16, when the number of dense layers was fine-tuned further, the Probe recognition accuracy improved up to 80%, which was higher than the 78% in Table 15 for only fine-tuning both the feature number and the data-enhanced number.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **KNN** & **SVM** & **RF** & **XGB** & **MLP** & **CNN** \\ \hline Best Feature Number & 15 & 41 & 15 & 18 & 21 & 23 \\ \hline Best Data-Enhanced Number & 8000 & 8000 & 7000 & 6000 & 6000 & 8000 \\ \hline Probe Accuracy & 76\% & 75\% & 64\% & 55\% & 78\% & 72\% \\ \hline \end{tabular}
\end{table}
Table 15: Best data-enhanced numbers for all classifiers.  
Figure 11: Accuracy of probe attack detection for various layer numbers of model design for MLP and CNN.  
After the two-stage fine-tuning process, we fine-tuned the parameters of the numbers of feature values, the numbers of Probe data augmentations, and the depths of the neural network models, and finally obtained the hyperparameter table for the best Probe recognition accuracy, as shown in Table 17. From the results of the first-stage experiments in Tables 9 and 10, compared with the results of the second-stage experiments of fine-tuning the parameters in Table 17, we found that the Probe recognition accuracy for all of the classifiers improved significantly after the fine tuning of the parameters in the second stage. In particular, the MLP classifier could significantly improve the Probe recognition accuracy from 74% to 80% after fine tuning, which was the highest among all of the classifiers. Furthermore, the proposed WGAN-GP data enhancement with two stage fine tuning improved by 10%, 7%, and 13% over the untuned Gaussian noise enhancement, untuned SMOTE enhancement, and no enhancement, respectively.