## 2 Related Work
[24] developed a dataset based on accurate DNS logs and employed various machine-learning methods. Their findings, especially the superior performance of the CART algorithm, highlighted the importance of effective feature selection methods and profoundly impacted our approach to data classification and feature selection. [PERSON] et al. [25] emphasized the significance of measurement data in enhancing classifier performance, achieving a harmonic mean F1-Score with 99% accuracy using a random forest (RF) technique. Their methodology and results provided critical insights into optimizing classifier performance, steering our study toward developing robust and accurate classification models. As cloud computing becomes increasingly central due to data security concerns and cyber threats, the development of practical intrusion detection systems (IDSs) has gained importance. Machine learning-based IDSs are proficient at detecting network threats, yet they experience performance degradation with large datasets. [PERSON] et al. [26] have proposed a hybrid approach that addresses the challenges of imbalanced datasets by integrating SMOTE with various feature selection techniques. This approach, tested using an RF model, achieved 98% and 99% accuracy rates on the UNSW-NB15 and Kyoto datasets, respectively, demonstrating superior performance compared to similar studies. [PERSON] et al. [27] presented an advanced IDS for cloud environments in their study. This system enhances classification accuracy by optimizing feature selection and weighting the ensemble model using the crow search algorithm (CSA). The ensemble classifier incorporates machine and deep learning models, including RF, SVM, XGBoost, and Decision Tree (DT). Tested on NSL-KDD, Kyoto, and CSE-CIC-IDS-2018 datasets, the ensemble model achieved high accuracy, recall, and precision levels. The study particularly highlights the superiority of the ensemble model in terms of the false positive rate (FPR) and false negative rate (FNR). In recent years, the evolution of deep learning has introduced novel methodologies like the Deep Packet method [28; 29; 30]. Due to its capacity to uncover hidden network patterns, this method offers a blueprint for our study, allowing us to delve deeper into network behavior analysis. In summary, the existing body of research demonstrates significant progress in the application of machine learning techniques for firewall packet classification, with models like Random Forests (RFs), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNNs) showing high accuracy across various datasets. However, a common limitation across many of these studies is the insufficient focus on addressing class imbalance, a critical issue that can skew results and compromise the reliability of classification models. While methods such as under-sampling and basic preprocessing have been applied, they often fall short of effectively balancing the data. Our study builds upon these foundational works by integrating advanced data balancing techniques--SMOTE, ADASYN, and BorderlineSMOTE--into an Artificial Neural Network (ANN) framework. This approach not only enhances the accuracy and reliability of classification across all classes, including minority ones, but also sets a new benchmark in the field of firewall packet classification, addressing the gaps identified in previous research. Table 1 presents a comparative analysis of the success rates in using ANNs and various machine learning techniques for firewall packet classification across different datasets and feature sets. In conclusion, our study makes a significant contribution to the field of network security by addressing the critical challenge of firewall packet classification through the in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
novative combination of Artificial Neural Networks (ANNs) and advanced data balancing techniques, including SMOTE, ADASYN, and BorderlineSMOTE. Unlike previous studies, such as those by [PERSON] et al.