## 6 Conclusions  
This study has demonstrated the potential of deep learning-based intrusion detection systems in addressing the challenges posed by increasing network traffic volumes and evolving cyber threats. By comparing deep learning models such as MLP, CNN, and LSTM with traditional machine learning algorithms like logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees, we have shown that deep learning models can achieve competitive accuracy and adaptability. However, the selection of an appropriate model depends on the specific use case and system requirements. For high interpretability and low computational overhead, random forest emerges as a strong choice due to its explainability and effectiveness in structured intrusion detection tasks. On the other hand, LSTM is particularly well suited for sequential network traffic analysis, making it ideal for detecting time-dependent attack patterns. CNN can be leveraged for feature-rich intrusion detection tasks, where spatial relationships within network data play a crucial role. While deep learning models offer superior performance in many scenarios, they require significant computational resources, making them more suitable for large-scale deployments with adequate hardware support. The application of data preprocessing, feature engineering, and data balancing techniques, such as correlation analysis and SMOTE, has proven effective in enhancing the performance of these models. Moreover, the investigation of optimization strategies, including parallel processing, distributed training, and model compression, has highlighted the potential for improving deep learning models' computational efficiency and resource utilization. The findings of this research project emphasize the suitability of deep learning-based intrusion detection systems for large-scale network environments and their ability to adapt to the ever-evolving landscape of cyber threats. While the random forest and decision tree models demonstrated the best performance in this comparison, deep learning models,such as MLP, CNN, and LSTM, also showed promising results. As a result, these models represent a promising solution for the ongoing challenges faced by intrusion detection systems and can contribute to the overall security of network infrastructures. In light of the findings from this study, future research directions could focus on the following specific aspects:  
* Developing hybrid models that integrate the strengths of both deep learning and traditional machine learning algorithms, which could potentially enhance performance in detecting various types of cyber threats. * Exploring the use of unsupervised and semi-supervised learning techniques within deep learning-based intrusion detection systems to mitigate the challenges posed by the limited availability of labeled data and improve the models' adaptability to new and unknown threats. * Investigating advanced feature selection and extraction techniques, such as deep autoencoders and graph-based methods, to more effectively capture the underlying patterns and relationships in network traffic data. * Evaluating the impact of different hyperparameter tuning and model selection strategies on the performance of deep learning-based intrusion detection systems to optimize their effectiveness in real-world network settings. * Assessing the performance of deep learning models in adversarial conditions, such as the presence of sophisticated and stealthy attacks designed to evade detection, as well as the influence of noisy or incomplete data. Conceptualization, K.T., M.L.A. and S.S.; methodology, K.T., M.L.A., J.D. and S.S.; validation, K.T., M.L.A. and S.S.; formal analysis, K.T., J.D., D.D. and S.S.; investigation, K.T., M.L.A. and S.S.; writing--original draft preparation, K.T., M.L.A., J.D. and D.D. ; writing--review and editing, K.T., M.L.A., J.D., D.D. and S.S.; supervision, J.D., D.D. and K.T. ; project administration, S.S. and M.L.A. All authors have read and agreed to the published version of the manuscript.