Active Learning Framework For Long-term Network Traffic Classification

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21240/23:00366203

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Active Learning Framework For Long-term Network Traffic Classification

Popis výsledku v původním jazyce

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&amp;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.

Název v anglickém jazyce

Active Learning Framework For Long-term Network Traffic Classification

Popis výsledku anglicky

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&amp;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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC

ISBN

979-8-3503-3286-5

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

893-899

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Las Vegas, USA

Datum konání akce

8. 3. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000995182600138