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

Result code in 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>
Alternative codes found
RIV/68407700:21240/24:00379141
Result on the web
<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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Result language
angličtina
Original language name
Workshop on using Machine Learning in Network Traffic Classification and how to avoid common pitfals
Original language description
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.
Czech name
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Czech description
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Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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