Towards reusable models in traffic classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133681" target="_blank" >RIV/63839172:_____/24:10133681 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/24:00376947
Result on the web
<a href="https://ieeexplore.ieee.org/document/10559009" target="_blank" >https://ieeexplore.ieee.org/document/10559009</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/TMA62044.2024.10559009" target="_blank" >10.23919/TMA62044.2024.10559009</a>
Alternative languages
Result language
angličtina
Original language name
Towards reusable models in traffic classification
Original language description
The machine learning communities, such as those around computer vision or natural language processing, have developed numerous supportive tools. In contrast, the network traffic classification field falls behind, and the lack of standard datasets and model architectures holds the entire field back. This paper aims to address this issue. We introduce CESNET Models, a package comprising pre-trained deep learning models tailored for traffic classification. The included models are trained on public datasets for the task of web service classification. Using the new package, researchers and practitioners can skip model design from scratch and the collection of large datasets but instead focus on fine-tuning and adapting the models to their specific needs, thus accelerating the pace of research and development in network traffic classification.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
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)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 8th Network Traffic Measurement and Analysis Conference
ISBN
978-3-903176-64-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway , USA
Event location
Dresden, Germany
Event date
May 21, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001258591000010