## 4 Results
The baseline ANN model, without any data balancing techniques, exhibits the highest latency at 0.355833 s per prediction and the lowest throughput at 360.58 samples per second, indicating slower processing and less efficiency in handling large volumes of data in real-time scenarios. On the other hand, the ANN models integrated with data balancing techniques, such as SMOTE, BorderlineSMOTE, and ADASYN, demonstrate significant improvements in real-time performance. The ANN + BorderlineSMOTE model, in particular, achieves the highest throughput of 510.53 samples per second with a markedly lower latency of 0.107312 s per prediction. This suggests that BorderlineSMOTE not only enhances the model's classification accuracy but also optimizes its efficiency in real-time processing. Similarly, the ANN + ADASYN model achieves the lowest latency among the tested models, at 0.089748 s per prediction, along with a robust throughput of 444.19 samples per second. The ANN + SMOTE model also shows considerable gains, with a latency of  
Figure 12: Accuracy and loss graphs of training and validation metrics of ANNs + BorderlineSMOTE analysis. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Latency Per Prediction** & **Throughput (Samples/Second)** \\ \hline ANNs & 0.355833 s & 360.58 samples/second \\ ANNs + SMOTE & 0.107679 s & 406.59 samples/second \\ ANNs + BorderlineSMOTE & 0.107312 s & 510.53 samples/second \\ ANNs + ADASYN & 0.089748 s & 444.19 samples/second \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Real-time performance metrics of different ANN models for firewall packet classification. 0.107679 s and a throughput of 406.59 samples per second, demonstrating the effectiveness of SMOTE in improving both classification performance and processing speed. Overall, these findings underscore the benefits of integrating advanced data balancing techniques with ANNs. Not only do these techniques enhance the accuracy of the models when handling imbalanced datasets but they also significantly improve real-time processing capabilities, making the models more suitable for deployment in time-sensitive network security environments. Table 9 provides a comparative analysis of various studies on firewall packet classification, highlighting the performance of different models across a range of datasets and feature sets. In comparison to other studies, the ANN model combined with BorderlineSMOTE in our investigation demonstrates a high level of accuracy at 97%, with a weighted average of 96%. This performance is notably strong, especially when considering the complex nature of the data and the challenges associated with class imbalance, which were effectively mitigated using BorderlineSMOTE. The comparison shows that our model's performance is competitive with other advanced classifiers, such as Random Forest (RF) and Support Vector Machine (SVM), which are commonly used in similar contexts. For instance, the Random Forest model in [PERSON] and [PERSON]'s [8] study achieved a slightly higher accuracy of 99.7%, though this model's recall was lower at 0.85, indicating that while it was highly precise, it may not have been as effective at identifying all relevant instances. Similarly, the study by [PERSON] [25] et al. reported a 99% accuracy using SVM, RF, k-NN, and Logistic Regression, which is slightly higher than our results but without addressing the specific challenges of imbalanced datasets that our study confronts. Furthermore, the diversity of models used across the different studies, such as Decision Trees (DTs), SVMs with various activation functions, and ensemble methods, highlights the complexity of achieving high accuracy in firewall packet classification. Despite this complexity, our study's results are robust, showcasing the efficacy of integrating ANNs with data balancing techniques like BorderlineSMOTE to achieve a balanced, high-performance model.