## 5 Conclusions  
This paper proposes an adaptive deep learning algorithm that determines the number of hidden layers and neurons of a neural network by extracting the dimension of the original data. By setting parameters to balance the detection time and output accuracy, the proposed ADL algorithm can adapt to different network environments and network sizes. Moreover, by combining the ADL algorithm with traditional machine learning algorithms, they can effectively discriminate between large ranges of network traffic, improve the accuracy of intrusion detection, converge faster, and reduce detection time significantly. In particular, Naive Bayes classification produces the worst performance as compared to KNN, DT and BPNN. After adding the proposed ADL to Naive Bayes classification, its performance can be improved significantly. For example, the accuracy of R2L is improved from 84.13% to 91.32%, the accuracy rate increased from 91.23% to 96.44%, the accuracy rate of U2L increased from 28.49% to 43.83%, and the accuracy rate increased from 66.88% to 75.02%. The proposed ADL algorithm can also improve the performance of the other three traditional classifiers to different extent. For future work, a real-time packet capture platform can be adopted for analysis and further optimisation of the proposed ADL algorithm.  
For future work, a real-time packet capture platform can be set up for further analysis and optimisation of the proposed ADL algorithm. In particular, the experiment utilised a combination of deep neural networks and traditional machine learning algorithms, with specific parameters set for different network sizes to reduce the training time of the network. However, the current process of parameter learning is resource-intensive in terms of computational power and computational time, and future research could optimise the learning time of parameters and the requirement for hardware computational power. In addition, to study the complex data patterns and low footprint stealth attacks of the contemporary network traffic, we plan to verify the performance of our proposed ADL algorithm using the UNSW-NB15 dataset [27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & \(\boldsymbol{s_{A}}\) **(\%)** & \(\boldsymbol{s_{P}}\) **(\%)** & \(\boldsymbol{s_{R}}\) **(\%)** & \(\boldsymbol{S_{F1}}\) **(\%)** \\ \hline Normal & 99.58 & 100 & 99.64 & 99.82 \\ \hline DoS & 99.76 & 100 & 99.81 & 99.90 \\ \hline Probe & 99.81 & 100 & 99.32 & 96.61 \\ \hline R2L & 24.36 & 100 & 88.36 & 93.83 \\ \hline U2R & 10.17 & 41.32 & 47.23 & 44.08 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results using the Asymmetric Convolutional Encoder algorithm.  
**Author Contributions:** Conceptualization, X.J.L. and M.M.; methodology, M.M.; software, Y.S. and M.M.; validation, Y.S., M.M. and X.J.L.; formal analysis, Y.S., M.M. and X.J.L.; writing--original draft preparation, X.J.L.; writing--review and editing, X.J.L. and M.M.; supervision, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.