<![CDATA[The increasing diversity and scale of network traffic introduce significant challenges in performing accurate and interpretable firewall analysis. This research aims to bridge the gap between predictive performance and model transparency by developing an explainable machine learning framework for multi-class firewall traffic classification. The study utilizes the Internet Firewall Data dataset consisting of 65,532 network traffic instances distributed across four firewall action classes and evaluates seven classification algorithms, including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, Naïve Bayes, and Logistic Regression. The dataset was partitioned using a stratified 80:20 hold-out approach to preserve the original class distribution and the experimental process involves data preprocessing, normalization, and validation on an independent test set using accuracy, precision, recall, and F1-score metrics. The findings reveal that XGBoost achieves the highest performance, reaching an accuracy of 99.81%, followed by Decision Tree and Random Forest. This indicates that ensemble and tree-based approaches are highly effective in modeling complex and non-linear traffic patterns. To improve interpretability, this study incorporates explainable artificial intelligence techniques, including feature importance and SHAP analysis. The results show that traffic-related attributes significantly influence classification outcomes, providing meaningful insights into firewall decision behavior]]>
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