Vision of Active Learning Framework Approach to Network Traffic Analysis Research
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00358729" target="_blank" >RIV/68407700:21240/22:00358729 - isvavai.cz</a>
Result on the web
<a href="https://pesw.fit.cvut.cz/2022/PESW_2022.pdf" target="_blank" >https://pesw.fit.cvut.cz/2022/PESW_2022.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Vision of Active Learning Framework Approach to Network Traffic Analysis Research
Original language description
Current research in the network security domain intensively uses machine learning (ML) and artificial intelligence to automate processes and reveal hidden patterns in data. These technologies, however, require lots of training datasets with ideally high quality. Additionally, network infrastructures continuously evolve and thus network traffic dynamically changes in time as well. There is an urgent need to adapt machine learning models, update datasets with the latest samples of annotated network traffic and retrain the models regularly to sustain feasible performance. Active Learning Framework (ALF) directly targets these demands and aims to provide a modular platform for scientific experiments and deployment in practice as well as to support research activities regarding quality of datasets. This paper particularly describes ALF software and proposes its possible use cases in research and practice domains.
Czech name
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Czech description
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Classification
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)
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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů