## 6 Conclusions  
Intrusion detection has significantly benefited from advancements in cyber security, particularly with the incorporation of ML and DL algorithms. This paper presented a novel technique for detecting intrusions in a cloud environment by combining ML and DL algorithms, explicitly utilizing a reduction algorithm based on the RF classifier for feature selection and the RBFNN for intrusion detection. The results obtained from our approach demonstrate its effectiveness in detecting intrusions, achieving an ACC rate higher than 94% and an FNR lower than 0.0831%. This showcases the capability of our model to identify and classify intrusions in the cloud environment accurately. Additionally, the utilization of feature selection methods has proved to be instrumental in enhancing the overall performance of the IDS. One notable strength of our model is its ability to achieve high ACC rates and reduce prediction time by utilizing a limited number of variables. Our model improves the ACC rate and enhances operational efficiency by leveraging carefully selected features.  
Moreover, our model successfully addressed the challenges posed by imbalanced datasets, such as the Bot-IoT dataset, by effectively balancing the classification of high-dimensional data. The feature selection approach helped increase the TN from 8.1% when using all features to 86% with only three selected features.  
In summary, our study presents a promising technique for intrusion detection in a cloud environment by combining ML and DL algorithms. The results validate the effectiveness of our approach, showcasing its potential for enhancing cyber security in cloud-based systems. With future advancements in feature engineering and dimensionality reduction, we anticipate even more significant improvements in the performance and efficiency of our model.  
As part of our future work, we aim to advance our feature engineering techniques by incorporating dimensionality reduction methods. This enhancement will enable our model to perform even more efficiently by reducing the complexity of the input data. By exploring dimensionality reduction methods, we anticipate further improvements in the ACC and computational efficiency of our IDS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Methods** & **Dataset** & **ACC (\%)** \\ \hline
[37] & Ensemble learning & Bot-IoT & 99.99 \\ \hline
[44] & KNN & Bot-IoT & 99.99 \\ \hline
[14] & Gradient boosting & Bot-IoT & 100 \\  & DT & NSL-KDD & 100 \\ \hline Our proposed model & RF-RBFNN & Bot-IoT & 99.99 \\  & NSL-KDD & 94.16 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model’s performance comparison.  
**Author Contributions:** Conceptualization, H.A. and M.M.-e.; methodology, A.G.; software, M.M.-e.; validation, A.G., S.B. and M.A.; formal analysis, A.A.; investigation, N.A.; resources, A.G.; data curation, M.A.; writing--original draft preparation, H.A.; writing--review and editing, M.M.-e.; visualization, S.B.; supervision, A.G.; project administration, A.A.; funding acquisition, N.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Assessments and Experimental results, obtained using Anaconda 3 IDE, are available andwill be shared with authors at [[https://sites-Google.com/umi.ac.ma/azrour](https://sites-Google.com/umi.ac.ma/azrour)]([https://sites-Google.com/umi.ac.ma/azrour](https://sites-Google.com/umi.ac.ma/azrour)), accessed on 22 August 2023.  
**Conflicts of Interest:** The authors declare no conflict of interest.