Enhancing Network Security: Evaluating SDN-Enabled Firewall Solutions and Clustering Analysis Using K-Means through Data-Driven Insights

Enhancing Network Security: Evaluating SDN-Enabled Firewall Solutions and Clustering Analysis Using K-Means through Data-Driven Insights

attack patterns; data-driven analysis;K-Means clustering;network security;SDN-enabled firewalls

In the face of escalating and increasingly complex cyber threats, enhancing network security has become a critical challenge. This study addresses this issue by investigating the optimization of SDN-enabled firewall solutions using a data-driven approach. The research employs K-Means clustering to analyze attack patterns, aiming to identify and understand distinct patterns for improved firewall effectiveness. Through the clustering process, attack data was classified into three clusters:Cluster 0, indicating concentrated attack sources likely tied to high-activity regions or networks; Cluster 1, representing a disperseddistribution of attacks, pointing to diverse origins; and Cluster 2, linked to specific geographic regions or unique attack behaviors. The clustering efficacy was evaluated using the Silhouette Score (0.606) and the Davies-Bouldin Index (0.614), indicating meaningful and reliable clustering outcomes. These findings provide actionable insights into network threatpatterns, enabling the refinement and enhancement of SDN-enabled firewalls. The study contributes to the field by demonstrating the potential of clustering techniques in uncovering patterns overlooked by traditional methods and paving the way for further research into alternative clustering algorithms and broader applications in networksecurity

Ahmad Turmudi Zy1*,Isarianto2, Anggi Muhammad Rifai3, Agung Nugroho4, Abdul Ghofir5

Department of Informatics Engineering, Faculty of Engineering, Pelita Bangsa University, Bekasi, Indonesia

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