## 5 Conclusions  
The increase in the number and severity of network attacks in recent years has made APT detection a vital matter and it has become the key to network security protection. A large amount of security audit data and the complex and dynamic features of intrusion behavior, as well as optimization of the performance of APT detection has become an important open issue. This has attracted much attention from the information security and academic research communities. According to the experiments in this study, the classifiers SVM-RBF and MLP-AS (N = 4) have the best recognition rate for NSL-KDD. Using PCA to reduce the dimension did not help with the recognition rate, but it could improve the calculation speed. It is recommended that SVM-RBF or MLP-AS (N = 4) classifiers be used for the detection of an APT attack. There is an advantage to using SVM and PCA together to accelerate the calculation process. The experimental results of this study provide reference models for follow-up research in the selection of classifiers and parameters, information about the effects of the reduction of dimensionality and calculation speed, as well as a better understanding of the contents of the data set.  
Many of the network intrusion detection system modules used today are modeled using the support vector machine algorithms. However, they are very demanding in terms of system computing hardware performance. To alleviate this problem, dimension reduction is applied to a given data set that uses important feature extraction to improve processing speed. Data from the experimental results show that data dimensionality reduction had no significant impact on the results but detection speed was enhanced. Improvements in the characteristic information of the data set content used to analyze the APT attack, as well as in the dimensionality reduction method, will further improve the accuracy of effective analysis.  
[PERSON] conceived and designed the experiments; [PERSON]-[PERSON] contributed reagents/materials/analysis tools; [PERSON]-[PERSON] performed the experiments and analyzed the data; [PERSON]-[PERSON] wrote the paper.  
This research was funded by the Ministry of Science and Technology of the Republic of China, Taiwan, for financial support under Contract No. MOST 107-2221-E-027-116 and the APC was funded by National Taipei University of Technology.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Test Classification**} \\ \cline{2-5}  & \multicolumn{2}{c}{**First Set of Tests**} & \multicolumn{1}{c}{**Second Set of Tests**} & \multicolumn{1}{c}{**Third Set of Tests**} \\  & \multicolumn{2}{c}{**(Correct Rate/Time)**} & \multicolumn{1}{c}{**(Correct Rate/Time)**} & \multicolumn{1}{c}{**(Correct Rate/Time)**} \\ \hline \multirow{4}{*}{Naive Bayes} & Original data & 90\% & 81.28\% & 80.9\% \\  & (122 Features) & 0.67 (s) & 0.67 (s) & 0.61 (s) \\ \cline{2-5}  & Data after dimension & 91.54 & 79.94 & 79.46 \\  & reduction (94 Features) & 0.44 (s) & 0.64 (s) & 0.47 (s) \\ \hline \multirow{4}{*}{J48 (C = 0.25, M = 2)} & Original data & 59.3\% & 51.08\% & 44.2\% \\  & (122 Features) & 0.16 (s) & 0.2 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 48.32\% & 53.02\% & 50.22\% \\  & reduction (94 Features) & 0.13 (s) & 0.14 (s) & 0.11 (s) \\ \hline \multirow{4}{*}{MLP-AS (N = 4)} & Original data & 97.62\% & 85\% & 81.76\% \\  & (122 Features) & 0.17 (s) & 0.17 (s) & 0.17 (s) \\ \cline{1-1} \cline{2-5}  & Data after dimension & 97.82\% & 84.56\% & 79.32\% \\ \cline{1-1}  & reduction (94 Features) & 0.17 (s) & 0.17 (s) & 0.19 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
**Acknowledgments:** The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financial support of this research under contract no. MOST 107-2221-E-027-116.  
**Conflicts of Interest:** The authors declare no conflict of interest.