## 1 Introduction
The research is motivated by two overarching goals:  
Data Preparation and Structuring: The first goal is to delineate a coherent and systematic approach for the preprocessing and structuring of raw network traffic data, making it amenable to the rigorous training of machine learning models. The study aims to elucidate the intricacies of data transformation, offering a structured pathway to convert raw network traffic data into an optimal format for training machine learning models. Model Evaluation and Result Interpretation: The second goal is to delve into the interpretative aspects of the model's outcomes, emphasizing seminal classification metrics such as precision, recall, F1-Score, and accuracy to ascertain the model's proficiency and reliability in classifying network packets. This research acknowledges the challenges posed by class imbalances, particularly in the'reset-both' category, and addresses these issues through the strategic integration of advanced data balancing techniques such as the Synthetic Minority Over-samplingTechnique (SMOTE), ADASYN, and BorderlineSMOTE. By incorporating these methods, the study significantly enhances the model's predictive accuracy and reliability across all classes, thereby advancing the overall effectiveness of firewall packet classification. In exploring the potential of Artificial Neural Networks (ANNs) within the domain of network packet classification, this research underscores the critical role of data resampling techniques in refining model performance. The study presents a forward-thinking, data-driven approach that bridges the fields of machine learning and network security, aligning with contemporary advancements in the field. It contributes to the evolution of network security by promoting more automated and precise methodologies for firewall packet classification. This research stands as a seminal contribution by integrating ANNs with SMOTE, ADASYN, and BorderlineSMOTE to develop a robust and efficient framework for firewall packet classification. By combining these advanced methodologies, the study transcends traditional approaches, offering improved solutions to the increasingly complex challenges inherent in network security. This structured approach not only refines the classification process but also enhances the broader understanding and application of machine learning techniques in cybersecurity. Addressing critical issues such as the exponential growth of data traffic, the rapid evolution of cyber threats, and the inherent challenges of data imbalance, the proposed framework significantly improves the accuracy, reliability, and real-time processing capabilities of firewall packet classification systems. Leveraging the adaptive learning capabilities of ANNs alongside the class-balancing strengths of SMOTE, ADASYN, and BorderlineSMOTE, this research provides comprehensive methodologies for data preprocessing, model training, and evaluation, ensuring solutions that are both practical and scalable. Ultimately, this work underscores the importance of merging theoretical advancements with practical implementations, setting a new benchmark for firewall packet classification and contributing to a more secure and resilient network environment.