## 6 Conclusions and Future Work  
In this research, we investigated feature and model overfitting reduction for network intrusion detection. We conducted our experiments on a subset of CSE-CIC-2018 dataset and analyzed feature reduction approaches, including Linear Regression, Boruta, Random Forest with IncMSE, Random Forest with IncNodePurity, LASSO, and autoencoders. We then fed the feature-reduced datasets into classifiers, including Decision Tree, Naive Bayes, neural network, Random Forest, and XGBoost. Our experiments show that the Decision Tree classifier on autoencoders-based reduced sets of features yields the lowest overfitting among all the combinations. For future work, we plan to leverage deep learning-based classifiers such as LSTM (Long Short-Term Memory) or convolutional neural networks (CNNs) to further enhance detection performance. Additionally, we aim to refine the autoencoder's structure for feature construction, aiming to achieve a higher detection rate while minimizing overfitting. Furthermore, we aspire to evaluate the impact of our proposed approaches on the detection rate of different classes of attack vectors separately. By understanding the strengths and weaknesses of our techniques on specific attack types, we can tailor our solution to be more robust and effective across various threat scenarios. Another aspect of our future research involves adapting our solution to handle class imbalance effectively. Real-world datasets exhibit imbalanced class distributions, where certain attack types might be significantly under-represented compared to others. Devising methods to handle class imbalance will be pivotal in ensuring the reliability and fairness of our model in practical deployment scenarios. Conceptualization, F.A.A., A.M.F. and S.K. ; methodology, F.A.A. and A.M.F. ; validation, F.A.A., A.M.F. and S.K. ; formal analysis, F.A.A. and A.M.F. ; investigation, F.A.A. and A.M.F. ; writing--original draft preparation, F.A.A., A.M.F. and S.K. ; writing--review and editing, F.A.A., A.M.F. and S.K. ; visualization, F.A.A. and A.M.F. ; supervision, A.M.F. and S.K. ; project administration, A.M.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. * [10] Intrusion Detection Evaluation Dataset (CIC-IDS2017). Available online: [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed on 6 July 2023). * [11] CSE-CIC-IDS2018 on AWS. Available online: [[https://www.unb.ca/cic/datasets/ids-2018.html](https://www.unb.ca/cic/datasets/ids-2018.html)]([https://www.unb.ca/cic/datasets/ids-2018.html](https://www.unb.ca/cic/datasets/ids-2018.html)) (accessed on 6 July 2023). * [12] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An ensemble method based on selection using bat algorithm for intrusion detection. _J._**2018**, _61_, 526-538. [CrossRef]
* [13] [PERSON]; [PERSON]; [PERSON]; [PERSON] Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. In Proceedings of the 2018 Network and Distributed System Security Symposium (NDSS)., Internet Society, San Diego, CA, USA, 18-21 February 2018. [CrossRef]
* [14] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Towards Detecting and Classifying Network Intrusion Traffic Using Deep Learning Frameworks. _J. Internet Serv. Inf. Secur._**2019**, \(9\), 1-17. * [15] [PERSON]; [PERSON]; [PERSON] 2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes. In _Proceedings of the Web, Artificial Intelligence and Network Applications_; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; Springer: Cham, Switzerland, 2020; pp. 687-696. * [16] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Inter-dataset generalization strength of supervised machine learning methods for intrusion detection. _J. Inf. Secur. Appl._**2020**, _54_, 102564. :: 10.1016/j.jisa.2020.102564.