Towards reusable models in traffic classification

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21240/24:00376947

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards reusable models in traffic classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Towards reusable models in traffic classification

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/VJ02010024" target="_blank" >VJ02010024: Analýza šifrovaného provozu pomocí síťových toků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 8th Network Traffic Measurement and Analysis Conference

ISBN

978-3-903176-64-5

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway , USA

Místo konání akce

Dresden, Germany

Datum konání akce

21. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001258591000010