# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 6 Conclusions and Future Work  
Cloud computing has revolutionized the field of information technology with its vast applications. However, despite the use of various cybersecurity methods, cyberattacks in cloud environments are on the rise. To ensure the security of these systems, it is essential to find solutions and measures to reduce these attacks. One of the critical cybersecurity defenses against cyberattacks in a cloud computing environment is an intrusion detection system (IDS). However, current IDSs must be equipped to handle the vast amounts of analyzed data and real-time detection, reducing their performance. Therefore, in our work, we propose a deep learning model to improve the security of cloud computing environments by detecting and classifying cyberattacks. This research thoroughly examined a deep learning model based on CNNs for network traffic classification in cloud computing environments. With cyber threats becoming increasingly sophisticated, security poses significant challenges. By utilizing the CSE-CICIDS2018 dataset in various scenarios, the study demonstrated the model's exceptional ability to effectively differentiate between benign and malicious traffic with high accuracy. These findings highlight the potential of advanced deep learning techniques to significantly enhance traditional IDSs, which are crucial for securing cloud environments. The implications of this study are significant. Implementing this CNN-based model could significantly improve the accuracy and reliability of IDSs within cloud systems by reducing false positives and effectively identifying diverse cyber threats. It contributes significantly to the theoretical landscape of cybersecurity, showcasing how deep learning can overcome some limitations of traditional IDSs, particularly in dynamic and complex cloud computing settings. The proposed deep learning-based approach for intrusion detection consists of seven major stages. At first, we acquired a publicly available dataset regarding intrusion detection in the cloud environment. We preprocessed the collected dataset in the second stage to remove missing or null values. Similarly, we performed label encoding and data standardization. We used Pearson correlation matrix analysis in the third stage to select useful features and efficiently train the deep learning models. After that, we used the SOMTE oversampling strategy and down-sampling to obtain a balanced dataset and then split the data into training and testing sets. We trained and tested deep learning models for classifying cyberattacks in the fifth and sixth stages. Finally, we compared the performance of trained deep learning models and proposed the best-performing model for efficient intrusion detection in a cloud environment. Future works can take several directions to further enhance the effectiveness and applicability of deep learning models in cloud computing cybersecurity. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Year** & **Learning Algorithm** & **Accuracy** \\ \hline
[15] & 2020 & DNN & 95\% \\
[33] & 2020 & Fully Connected Dense DNN & 90\% \\
[34] & 2021 & Autoencoder & 95.79\% \\
[35] & 2023 & MLP with BP & 98.41\% \\
[36] & 2022 & PCA-DNN & 97.77\% \\
[37] & 2022 & CNN & 98.15\% \\ Proposed & 2024 & Multi-Blocks of CNN & 98.67\% \\ \hline \end{tabular}
\end{table} 
Table 21: Comparison of the latest research on DL for IDSs based on the CES-CICIDS2018 dataset. These include the following:  
* Testing the CNN model in a live cloud environment can help assess its performance in real-time scenarios and understand the practical challenges and effectiveness of the model under operational conditions. * Exploring more optimization strategies, such as genetic algorithms, could improve the efficiency and accuracy of the CNN model. * Investigating the model's resilience against adversarial attacks designed to deceive machine learning models can lead to more robust cybersecurity defenses.