Towards Evaluating Quality of Datasets for Network Traffic Domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F21%3A10133379" target="_blank" >RIV/63839172:_____/21:10133379 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/21:00353111 RIV/00216305:26230/21:PU147764

Výsledek na webu

<a href="http://dx.doi.org/10.23919/CNSM52442.2021.9615601" target="_blank" >http://dx.doi.org/10.23919/CNSM52442.2021.9615601</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/CNSM52442.2021.9615601" target="_blank" >10.23919/CNSM52442.2021.9615601</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Evaluating Quality of Datasets for Network Traffic Domain

Popis výsledku v původním jazyce

This paper deals with the quality of network traffic datasets created to train and validate machine learning classification and detection methods. Naturally, there is a long epoch of research targeted at data quality; however, it is focused mainly on data consistency, validity, precision, and other metrics, which are insufficient for network traffic use-cases. The rise of Machine learning usage in network monitoring applications requires a new methodology for evaluation datasets. There is a need to evaluate and compare traffic samples captured at different conditions and decide the usability of the already captured and annotated data. This paper aims to explain a use case of dataset creation, propose definitions regarding the quality of the network traffic datasets, and finally, describe a framework for datasets analysis.

Název v anglickém jazyce

Towards Evaluating Quality of Datasets for Network Traffic Domain

Popis výsledku anglicky

This paper deals with the quality of network traffic datasets created to train and validate machine learning classification and detection methods. Naturally, there is a long epoch of research targeted at data quality; however, it is focused mainly on data consistency, validity, precision, and other metrics, which are insufficient for network traffic use-cases. The rise of Machine learning usage in network monitoring applications requires a new methodology for evaluation datasets. There is a need to evaluate and compare traffic samples captured at different conditions and decide the usability of the already captured and annotated data. This paper aims to explain a use case of dataset creation, propose definitions regarding the quality of the network traffic datasets, and finally, describe a framework for datasets analysis.

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

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2021 17th International Conference on Network and Service Management

ISBN

978-3-903176-36-2

ISSN

2165-963X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

264-268

Název nakladatele

IEEE

Místo vydání

Piscataway , USA

Místo konání akce

Izmir, Turecko

Datum konání akce

25. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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