# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 5 Discussion  
Various experimental scenarios have demonstrated the suitability of using CNNs for IDS in cloud environments. With their exceptional feature extraction and ability to learn complex data without manual intervention, and because of their ability to process large-scale and high-dimensional data, CNNs are pivotal in identifying subtle signs of cyberattacks. Table 20 provides performance metrics for all seven cyber scenarios to illustrate the model's effectiveness across different types of cyberattacks. The scenarios encompass a range of attacks, from brute force attack simulations and DoS attack types to advanced DDoS techniques, web application attacks, infiltration tactics, and botnet detection. Notably, the model demonstrates near-perfect accuracy, precision, recall, and F1-scores in most scenarios, such as scenario 1 (\"Brute Force Attack Simulation\") (99.94%) and scenario 2 (\"DoS Attack Types\") (99.95%). Particularly impressive are the perfect scores in scenario 4 (\"Advanced DDoS Techniques\") and scenario 7 (\"Botnet Detection\"), each achieving 100% across all metrics, indicating flawless identification and classification of these sophisticated threats. In contrast, scenario 3 (\"DdoS LOIC-HTTP\") shows slightly lower, but still highly commendable, metrics with an accuracy of 99.27%. This suggests a minimal, yet existent, margin for error. Similarly, scenario 5, (\"Web Application Attacks\") exhibits high performance, with an accuracy of 99.29% and a notable precision of 99.4%, slightly higher than its recall of 99.18%, pointing toward a strong detection rate with minimal false positives. The most significant drop in performance is observed in scenario 6 (\"Infiltration Tactics\"), where the accuracy is 92.43% and the precision is slightly higher at 93.08%. This indicates a relatively greater challenge in accurately identifying infiltration tactics compared to other attack types, though the performance remains robust. After analyzing various scenarios, it has been found that CNNs are highly effective in detecting threats in cloud computing environments. The model has demonstrated consistently high precision and recall values, especially for complex and sophisticated attacks. Its ability to adapt to attacks and its reliability in real-time attack detection and classification are noteworthy. Table 18 presents the evaluation results for all seven scenarios of the proposed model, along with the average performance across the entire model for multi-classification using a CNN-based deep learning approach on the CSE-CICIDS2018 dataset within a cloud computing environment. \begin{table}
\begin{tabular}{l l l} \hline
**Layer** & **Output Shape** & **Parameters** \\ \hline Total params & 95,810 & Memory Size: 374.26 KB \\ \hline Trainable params & 95,426 & Memory Size: 372.76 KB \\ Non-trainable params & 384 & Memory Size: 1.50 KB \\ \hline Total FLOPs & 559,507 & \\ \hline \end{tabular}
\end{table} 
Table 19: _Cont._Comparison with the State-of-the-Art  
During this discussion, it was found that applying CNNs to IDSs in cloud computing environments using the CSE-CICIDS2018 dataset has led to a significant advancement in cybersecurity measures. It highlights the superior performance of the proposed multi-blocks of the CNN model, which includes some strategies such as the SMOTE balance data strategy to avoid imbalance class issues and using a Dropout layer to mitigate the overfitting problem. This model achieved an exceptional accuracy of 98.67%. Compared to other models listed in Table 1, our approach effectively manages common issues such as data imbalance and feature redundancy, leading to improved robustness and reliability. Moreover, utilizing CNNs allows our model to capture complex patterns and anomalies in the dataset, outperforming traditional and hybrid methods that lack this capability. Our integrated and methodical approach provides significant advantages in accuracy, efficiency, and scalability, making it a superior solution for intrusion detection in modern cybersecurity landscapes.