DataZoo: Streamlining Traffic Classification Experiments
This addresses a gap for researchers in network traffic classification by offering incremental improvements in dataset accessibility and reproducibility.
The paper tackles the lack of standardized benchmark datasets and tools in network traffic classification by introducing DataZoo, a toolset that streamlines dataset management and reduces evaluation errors, providing a standardized API for three extensive datasets and methods for realistic partitioning.
The machine learning communities, such as those around computer vision or natural language processing, have developed numerous supportive tools and benchmark datasets to accelerate the development. In contrast, the network traffic classification field lacks standard benchmark datasets for most tasks, and the available supportive software is rather limited in scope. This paper aims to address the gap and introduces DataZoo, a toolset designed to streamline dataset management in network traffic classification and to reduce the space for potential mistakes in the evaluation setup. DataZoo provides a standardized API for accessing three extensive datasets -- CESNET-QUIC22, CESNET-TLS22, and CESNET-TLS-Year22. Moreover, it includes methods for feature scaling and realistic dataset partitioning, taking into consideration temporal and service-related factors. The DataZoo toolset simplifies the creation of realistic evaluation scenarios, making it easier to cross-compare classification methods and reproduce results.