## 5 Experimental Results and Analysis
### Experimental Setup  
In the proposed MDPCA-DBN model, the MDPCA divides the training dataset into several subsets according to the feature similarity. An unreasonable subset will seriously affect the detection \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Category**} & **Training Dataset** & **Testing Dataset** \\ \cline{2-3}  & **UNSW\_NB15\_Training-Set 20\%** & **UNSW\_NB15\_Testing-Set 20\%** \\ \hline Normal & 11,200 & 7400 \\ Generic & 8000 & 3774 \\ Exploits & 6679 & 2226 \\ Fuzzers & 3637 & 1212 \\ DoS & 2453 & 818 \\ Reconnaissance & 2098 & 699 \\ Analysis & 400 & 135 \\ Backdoor & 349 & 117 \\ Shellcode & 227 & 76 \\ Worms & 26 & 9 \\ \hline
**Total** & 35,069 & 16,466 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The class distribution of the UNSW-NB15 subset. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Category**} & **Training Dataset** & **Testing Dataset** \\ \cline{2-3}  & **KDDTrain+.20 Percent** & **KDDTest+** & **KDDTest-21** \\ \hline Normal & 13,449 & 9711 & 2152 \\ Probe & 2289 & 2421 & 2402 \\ DoS & 9234 & 7458 & 4342 \\ U2R & 11 & 200 & 200 \\ R2L & 209 & 2754 & 2754 \\ \hline
**Total** & 25,192 & 22,544 & 11,850 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The class distribution of the NSL-KDD dataset. performance of the proposed model. The number of clusters \(K\) determines the division of clustering subsets and directly affects the detection performance. If the value of \(K\) is too large, the integrity of the original dataset will be destroyed and the detection accuracy will decrease. The appropriate \(K\) can divide the original dataset into more reasonable subsets and, to a certain extent, break the imbalance of the training dataset, thus achieving higher detection accuracy. We calculated the decision graph and \(\gamma\) sorting graph of the MDPCA algorithm according to Equation (10), and determined the number of subsets according to the number of discrete points. Figures 5 and 6 show the decision graph and \(\gamma\) sorting graph on the NSL-KDD and UNSW-NB15 datasets, respectively. Figures 5a and 6a show the relationship between the local density \(\rho\) and distance \(\delta\) of each training sample. Figures 5b and 6b show the \(\gamma_{i}=\rho_{i}\delta_{i}\) graph sorted in descending order. [PERSON] et al. [PERSON] et al. (2019) pointed out that the clustering centers have a high local density \(\rho\) and maximum distance \(\delta\), that is, those with larger \(\gamma\) values are clustering centers. The descending order of \(\gamma\) values provides the number of clustering centers to be selected. The \(\gamma\) value of non-clustering centers is relatively smooth, while the \(\gamma\) value from the non-clustering center to the clustering center has a significant jump. As can be seen from Figure 5b, there are two discrete jump points (marked red and green), so the optimal number of clusters on the NSL-KDD dataset is 2, and the data points corresponding to the red and green points are clustering centers. In Figure 6b, there are two discrete jump points (marked red and green) with large spacing, so the optimal clustering number on the UNSW-NB15 dataset is 2, and the data points corresponding to the red and green points are clustering centers. Figure 5: Decision graph and \(\gamma\) sorting graph of MDPCA algorithm on the NSL-KDD dataset. Figure 6: Decision graph and \(\gamma\) sorting graph of MDPCA algorithm on the UNSW-NB15 dataset. Based on the experiment, we used the network structure and parameters that achieved the highest detection accuracy. The optimal network structures of the proposed model on the NSL-KDD and UNSW-NB15 datasets are 122-40-20-10-5 and 196-80-40-20-10, respectively. The Gaussian kernel parameter \(\sigma\) projects the original dataset into high dimensional space so that the spatial distribution of the original dataset is changed. The Gaussian kernel parameter \(\sigma\) varies between 1 and 300.