Abstract

This study examines the classification of HTTPS traffic using packet burst statistics, a crucial aspect of modern internet usage with significant implications for network security, traffic management, and service quality. Utilizing extensive datasets from real backbone networks, HTTPS traffic is categorized into five primary types: Live Video Streaming, Video Player, Music Player, File Uploading/Downloading, and Website & Other Traffic. Various machine learning algorithms are employed, with particular emphasis on Random Forest and XGBoost, which demonstrate high accuracy rates. Additionally, recent advancements such as the Kolmogorov-Arnold Network (KAN) method are incorporated for comparative analysis, enhancing the robustness of the study. A comprehensive methodology is presented for model performance comparison and clustering analysis. The findings have practical applications in network security, traffic management, and service quality enhancement. This research makes a significant contribution to the field, providing a foundation for future studies focused on more effective classification and management of HTTPS traffic.

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