Active Learning Framework For Long-term Network Traffic Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133626" target="_blank" >RIV/63839172:_____/23:10133626 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/23:00366203
Result on the web
<a href="https://ieeexplore.ieee.org/document/10099065" target="_blank" >https://ieeexplore.ieee.org/document/10099065</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CCWC57344.2023.10099065" target="_blank" >10.1109/CCWC57344.2023.10099065</a>
Alternative languages
Result language
angličtina
Original language name
Active Learning Framework For Long-term Network Traffic Classification
Original language description
Recent network traffic classification methods benefit from machine learning (ML) technology. However, there are many challenges due to the use of ML, such as lack of high-quality annotated datasets, data drifts and other effects causing aging of datasets and ML models, high volumes of network traffic, etc. This paper presents the benefits of augmenting traditional workflows of ML training&deployment and adaption of the Active Learning (AL) concept on network traffic analysis. The paper proposes a novel Active Learning Framework (ALF) to address this topic. ALF provides prepared software components that can be used to deploy an AL loop and maintain an ALF instance that continuously evolves a dataset and ML model automatically. Moreover, ALF includes monitoring, datasets quality evaluation, and optimization capabilities that enhance the current state of the art in the AL domain. The resulting solution is deployable for IP flow-based analysis of high-speed (100 Gb/s) networks, where it was evaluated for more than eight months. Additional use cases were evaluated on publicly available datasets.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC
ISBN
979-8-3503-3286-5
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
893-899
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Las Vegas, USA
Event date
Mar 8, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000995182600138