DataZoo: Streamlining Traffic Classification Experiments
Jan Luxemburk Karel Hynek
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.