Analysis of Statistical Distribution Changes of Input Features in Network Traffic Classification Domain

This study investigates the evolving landscape of network traffic monitoring, which is crucial for maintaining computer network services and security. Traditional methods like Deep Packet Inspection (DPI) face challenges due to increased privacy protection through encryption, prompting a shift towards statistical-based detection using Machine Learning (ML). On the other hand, ML struggles with long-term evaluation due to various distribution changes. This study focuses on the CESNET-TLS-Year22 dataset, derived from one year of TLS network traffic on the CESNET2 backbone. Described research explores the behavior of modern protocols in real-world scenarios and their impact on dataset quality. The main result of our analysis is the identification of the Weekend phenomenon in network traffic classification that is generally overlooked during ML model training.