## 6 Discussion  
The findings of this study offer valuable insights into the application of Artificial Neural Networks (ANNs) combined with advanced data balancing techniques--specifically SMOTE, ADASYN, and BorderlineSMOTE--for the classification of firewall packets. The results not only demonstrate the robustness of ANNs in this context but also emphasize the critical importance of addressing class imbalance to enhance model performance across all categories. Our analysis revealed that integrating ANNs with BorderlineSMOTE resulted in the most effective model, achieving an accuracy of 97% with a weighted average precision and recall of 96%. This model's strong and consistent performance across all classes, including minority ones, is further underscored by its real-time efficiency, as evidenced by the latency and throughput metrics in Table 8. With a latency of 0.107312 s per prediction and a throughput of 510.53 samples per second, the ANN + BorderlineSMOTE model not only achieves high classification accuracy but also demonstrates superior real-time processing capabilities, making it a robust tool for practical network security applications. In comparison to prior studies, such as the Random Forest (RF) model used by [PERSON] and [PERSON] [8], which achieved slightly higher accuracy at 99.7%, our approach offers a more balanced performance across all classes, particularly those that are underrepresented. The RF model's lower recall (0.85) indicates potential struggles with accurately identifying minority classes, a challenge our model effectively addresses through the use of BorderlineSMOTE. [PERSON] and [PERSON] [9] explored Support Vector Machines (SVMs) with different activation functions, achieving a high recall of 0.985 with the sigmoid function, but their results were less consistent across metrics. In contrast, our model with BorderlineSMOTE maintained a more balanced performance, achieving high precision, recall, and F1-Scores across all classes. Furthermore, our model exhibited lower latency and higher throughput compared to traditional methods, making it more suitable for real-time applications. The study by [PERSON] [19], which reported a 99.839% accuracy using Decision Trees (DTs), highlights the necessity of addressing class imbalance, an area that our researchspecifically targeted. While their model achieved high accuracy, it did not address the complexities of imbalanced data, which are critical for ensuring robust model performance. Our findings indicate that the integration of data balancing techniques like BorderlineSMOTE can significantly improve the model's ability to handle such challenges, as reflected in both the classification accuracy and real-time performance. Similarly, [PERSON] et al. [25] achieved 99% accuracy using a combination of SVM, Random Forest, k-NN, and Logistic Regression. However, their lack of focus on class imbalance limits the generalizability of their findings to datasets with underrepresented classes. Our approach, which integrates ANNs with data balancing techniques, particularly BorderlineSMOTE, provides a more comprehensive solution, ensuring balanced classification across all classes and demonstrating efficiency in real-time processing. [PERSON] et al. [31] employed various classifiers, achieving 99.8% accuracy with a stacking ensemble, but the direct handling of class imbalance through techniques like BorderlineSMOTE, as demonstrated in our study, offers a more targeted approach to ensuring consistent performance across all classes, particularly the minority ones. Additionally, our model's favorable real-time metrics highlight its practicality in real-world security scenarios where quick and accurate decision-making is essential. The comparison with prior studies underscores the importance of not only achieving high accuracy but also ensuring that models perform consistently across all classes, particularly in imbalanced datasets.