## 7 Conclusions and Outlooks  
In this study, we conducted four experiments based on the MLP-BP and MLP-PSO models for binary and multi-class classification on the CSE-CIC-IDS-2018 dataset. We presented a list of related works and discussed them. Furthermore, we provided an analysis of the CSE-CIC-IDS-2018 dataset and details of our DNN architecture, data preparation, data preprocessing, feature importance, feature selection, and proposed models.  
Additionally, we described the details of the hardware resources and software environments used in this study, in addition to the most important libraries, classes, modules, functions, and algorithms used in our experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FPR** & **FNR** \\ \hline MLP-BP (Binary Classification) & 98.97\% & 1.03\% & 99.98\% & 98.80\% & 99.38\% & 0.13\% & 1.20\% \\ MLP-PSO (Binary Classification) & 96.25\% & 3.75\% & 98.75\% & 96.80\% & 97.76\% & 6.48\% & 3.20\% \\ MLP-BP (Multi-class Classification) & 98.41\% & 1.59\% & 99.55\% & 98.85\% & 99.20\% & 0.45\% & 1.15\% \\ MLP-PSO (Multi-class Classification) & 95.32\% & 4.68\% & 98.97\% & 96.27\% & 97.60\% & 1.03\% & 3.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 19: Overall performance scores of the proposed models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
[8] & 97.90\% & 98.00\% & 98.00\% & 98.00\% \\
[9] & 96.99\% & 97.46\% & 96.97\% & 97.04\% \\
[10] & 98.38\% & 97.79\% & 97.74\% & 97.76\% \\
[11] & 95.79\% & 95.38\% & 95.79\% & 95.11\% \\
[12] & 97.77\% & 97.94\% & 97.53\% & 97.73\% \\
[13] & 97.93\% & 99.97\% & 97.42\% & 98.68\% \\ Proposed MLP-PSO & 96.25\% & 98.75\% & 96.80\% & 97.76\% \\ Proposed MLP-BP & 98.97\% & 99.98\% & 98.80\% & 99.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 20: The best performance scores of the proposed models compared with related works.  
Finally, we provided and discussed the results. The best accuracy for binary classification was 98.97% and that for multi-class classification was 98.41% based on MLP-BP. Furthermore, we tried to provide some essential tips that we think are useful to researchers in this field. Furthermore, our proposed models showed an improvement in performance metrics scores, which we compared with those in related works.  
In the future, we plan to run our IDSs in a cloud computing environment and evaluate the detection efficiency in real time. Furthermore, we propose using another approach or a different metaheuristic optimization algorithm to improve the results.  
Methodology, S.A.; Software, S.A.; Validation, S.E.K.; Writing--original draft, S.A.; Writing--review & editing, S.E.K.; Supervision, S.E.K. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge Qassim University, represented by the Deanship of \"Scientific Research, on the financial support for this research under the number (COC-2022-1-2-J-29853) during the academic year 1444 AH / 2022 AD\".  
Not applicable.  
The authors declare no conflict of interest.