## 4 Results
The weighted and macro averages, both at 85%, reflect a more balanced yet less robust performance across all classes compared to the SMOTE results. These findings suggest that while ADASYN contributes to addressing class imbalance, it may not be as effective as SMOTE in enhancing the model's overall classification accuracy and stability across all classes. Further optimization or the exploration of alternative methods may be necessary to achieve higher performance levels, particularly for the more challenging minority classes. The Figure 9 confusion matrix, coupled with the performance metrics in Table 6, offers a detailed insight into the model's classification capabilities following the application of the ADASYN function. The model exhibits outstanding accuracy in predicting the \"allow\" and \"drop\" classes, with near-perfect precision and recall values, as evidenced by minimal misclassifications. However, the matrix reveals a notable challenge in distinguishing between the \"deny\" and \"reset-both\" classes, where a significant number of \"deny\" instances are incorrectly classified as \"reset-both\". This misclassification is reflected in the lower recall and F1-Scores for the \"deny\" class, indicating that while the model is generally effective, there is a clear need for further refinement to enhance its ability to accurately differentiate between these classes. These results suggest that while the model performs well overall, targeted improvements are necessary to address these specific classification challenges and improve its robustness in more complex scenarios. Figure 10 illustrates the training and validation accuracy and loss curves across 50 epochs for the ANN model enhanced with the ADASYN function. The accuracy graph shows a steady increase in both training and validation accuracy, eventually converging around 85%, indicating that the model achieves a high level of generalization without overfitting. The close alignment of the training and validation curves further suggests that the model maintains consistency in its performance across both the training and validation datasets. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline allow & 1.00 & 0.99 & 0.99 & 6024 \\ deny & 0.78 & 0.60 & 0.68 & 6189 \\ drop & 0.88 & 1.00 & 0.94 & 5988 \\ reset-both & 0.75 & 0.83 & 0.79 & 5875 \\ accuracy & & & 0.85 & 24,076 \\ weighted avg & 0.85 & 0.86 & 0.85 & 24,076 \\ macro avg & 0.85 & 0.85 & 0.85 & 24,076 \\ \hline \end{tabular}
\end{table} 
Table 6: Numerical results of the experimental study with ADASYN function. On the loss graph, both training and validation losses decrease significantly during the initial epochs, stabilizing around a similarly low value after approximately 20 epochs. The minimal gap between the training and validation loss curves, along with their convergence, indicates that the model is learning effectively, with no signs of significant overfitting or underfitting. Overall, these graphs confirm that the ANN model, when combined with ADASYN, performs robustly, achieving a balanced and reliable classification across the imbalanced dataset. Table 7 summarizes the performance metrics for the ANN model enhanced with the BorderlineSMOTE function, highlighting its effectiveness in classifying across different categories. The model achieves exemplary results, particularly in the \"allow\" and \"reset-both\" classes, with F1-Scores of 1.00 and 0.98, respectively, reflecting nearly perfect precision and recall. The \"deny\" class also performs strongly, with an F1-Score of 0.95, showcasing the model's ability to handle this category with high accuracy despite the inherent complexity. The \"drop\" class, though with a lower support, still attains a notable F1-Score of 0.92, supported by a perfect recall, demonstrating the model's robustness even with fewer instances. Figure 10: Accuracy and loss graphs of training and validation metrics of ANNs + ADASYN analysis.