Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues

Abstract

The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks.

Keywords

• network traffic classification
• concept drift
• knowledge transferring
• model distillation
• dataset distillation