DataZoo: Streamlining Traffic Classification Experiments
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 30 Oct 2023.
Builds on
9 of 26 references.
References · 9
- 2020Energy-based Out-of-distribution Detectioncited by 1,299
- 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
- 2017Robust Smartphone App Identification Via Encrypted Network Traffic Analysiscited by 16
- 2021Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applicationscited by 14
- 2022Fine-grained TLS services classification with reject optioncited by 14
- 2016Deciphering Malware's use of TLS (without Decryption)cited by 9
- 2019Large-scale Mobile App Identification Using Deep Learningcited by 7
- 2022A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classificationcited by 6
- 2021Thinkback: Task-SpecificOut-of-Distribution Detectioncited by 1
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Citing papers per year: 2025: 2.
2 papers.