# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 5 Discussion
Table 21 lists other research that has dealt with IDSs and used the same CSE-CICIDS2018 dataset with different deep learning approaches. It is important to note that the approaches resulted in slightly lower accuracies for models such as fully connected Dense DNN [15,33] and autoencoder [34], which achieved 90%, 95%, and 95.77%, respectively. These single-model applications usually struggle with complex and imbalanced datasets. However, the study's approach of combining robust data augmentation techniques with a hybrid deep learning model provides a significant improvement over traditional single-model applications, such as those existing in MLP with BP [35] and PCA-DNN [36], which achieved higher accuracies of 98.41% and 97.77%, respectively. The impact and operation of our proposed model for intrusion detection are significant, reflecting advancements in both methodological rigor and practical applicability. By employing the CNN paradigm, our model effectively identifies and mitigates network intrusions with high accuracy. Compared to the single-block CNN architecture in [37], which achieved 98.15% accuracy, our three-block CNN model significantly enhances feature extraction and performance, achieving a higher accuracy of 98.67%. The operation begins with the meticulous collection and preprocessing of the CSE-CICIDS2018 dataset, ensuring the data are clean and standardized, which is necessary for the integrity of the training process, separated into seven scenarios. Following this, the Pearson correlation coefficient matrix heatmap is utilized to select the most relevant features, optimizing the model's performance and reducing computational complexity. Our CNN model's ability to process and analyze high-dimensional data allows for the detection of intricate intrusion patterns that traditional methods might miss. Its deployment in a cloud environment highlights scalability and real-world applicability, making it capable of handling large-scale network data and evolving threats. It leads to superior detection rates, reduces \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Scenarios** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Scenario 1: & 99.94\% & 99.94\% & 99.94\% & 99.94\% \\ Brute Force Attack Simulation & & & & \\ Scenario 2: & 99.95\% & 99.95\% & 99.95\% & 99.95\% \\ DoS Attack Types & & & & \\ Scenario 3: & 99.27\% & 99.28\% & 99.27\% & 99.27\% \\ DDoS LOIC-HTTP & & & & \\ Scenario 4: & & & & \\ Advanced DDoS Techniques & & & & \\ Scenario 5: & 99.29\% & 99.4\% & 99.18\% & 99.3\% \\ Web Application Attacks & & & & \\ Scenario 6: & & & & \\ Infiltration Tactics & & & & \\ Scenario 7: & & & & \\ Botnet Detection & & & & \\ \hline Average Results & 98.69\% & 98.80\% & 99.68\% & 98.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 20: The overall evaluation performance of all 7 scenarios of the proposed model. false positives, and contributes to enhanced cybersecurity measures. Compared to existing models, our approach balances accuracy, efficiency, and operational practicality, making it a robust tool for modern intrusion detection systems.