DataZoo: Streamlining Traffic Classification Experiments

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133608" target="_blank" >RIV/63839172:_____/23:10133608 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/23:00370023
Result on the web
<a href="https://dl.acm.org/doi/pdf/10.1145/3630050.3630176" target="_blank" >https://dl.acm.org/doi/pdf/10.1145/3630050.3630176</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3630050.3630176" target="_blank" >10.1145/3630050.3630176</a>

Result language
angličtina
Original language name
DataZoo: Streamlining Traffic Classification Experiments
Original language description
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. 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.
Czech name
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Czech description
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Project
<a href="/en/project/VJ02010024" target="_blank" >VJ02010024: Flow-based Encrypted Traffic Analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SAFE '23: Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking
ISBN
979-8-4007-0449-9
ISSN
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e-ISSN
—
Number of pages
5
Pages from-to
3-7
Publisher name
ACM
Place of publication
New York
Event location
Paříž, Francie
Event date
Dec 5, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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