Workshop on using Machine Learning in Network Traffic Classification and how to avoid common pitfals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00378335" target="_blank" >RIV/68407700:21240/24:00378335 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/24:00379141

Výsledek na webu

<a href="http://2024.necs-winterschool.disi.unitn.it/index.html" target="_blank" >http://2024.necs-winterschool.disi.unitn.it/index.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Workshop on using Machine Learning in Network Traffic Classification and how to avoid common pitfals

Popis výsledku v původním jazyce

This workshop offers researchers hands-on experience in applying machine learning to network traffic analysis, focusing on service classification using a publicly available dataset captured from a real-world network over one year, featuring over 200 services. Participants will explore practical methods for addressing challenges like data drift caused by evolving network conditions, gaining insights into common pitfalls and strategies to enhance model robustness. Combining theoretical understanding with real-world applications, this session equips attendees with the tools to advance their research in network analytics effectively and show them open challenges in this area.

Název v anglickém jazyce

Workshop on using Machine Learning in Network Traffic Classification and how to avoid common pitfals

Popis výsledku anglicky

This workshop offers researchers hands-on experience in applying machine learning to network traffic analysis, focusing on service classification using a publicly available dataset captured from a real-world network over one year, featuring over 200 services. Participants will explore practical methods for addressing challenges like data drift caused by evolving network conditions, gaining insights into common pitfalls and strategies to enhance model robustness. Combining theoretical understanding with real-world applications, this session equips attendees with the tools to advance their research in network analytics effectively and show them open challenges in this area.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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