QoD: Ideas about Evaluating Quality of Datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00344910" target="_blank" >RIV/68407700:21240/20:00344910 - isvavai.cz</a>

Výsledek na webu

<a href="https://pesw.fit.cvut.cz/2020/PESW_2020.pdf" target="_blank" >https://pesw.fit.cvut.cz/2020/PESW_2020.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

QoD: Ideas about Evaluating Quality of Datasets

Popis výsledku v původním jazyce

Importance of computer networks is raising every year. The reason is that we are connecting more and more devices, applications and our daily routines depends on connectivity. On the other hand, this is a great potential for attackers. They can hide their activities in complex network environment and steal valuable data. Without solid dataset, our evaluation score is misinterpreting the real score in production environment, and, therefore, proper datasets have essential role in research&development of any ML-based classifier or detector. The main motivation for this paper is to find a way how to evaluate quality of any dataset to estimate if it is good enough for ML experiments. To our best knowledge, there are only a few studies focused on quality evaluation of datasets with network traffic. For experiments, we selected datasets about DNS over HTTP (DoH) detection and URL classification problems that are already being elaborated. All metrics are calculated from dataset level. Impact of these metrics is evaluated on Random Forest (RF) model. We show results we have discovered in our datasets and ML detection modules. Eventually, we discuss possible next steps in this research.

Název v anglickém jazyce

QoD: Ideas about Evaluating Quality of Datasets

Popis výsledku anglicky

Importance of computer networks is raising every year. The reason is that we are connecting more and more devices, applications and our daily routines depends on connectivity. On the other hand, this is a great potential for attackers. They can hide their activities in complex network environment and steal valuable data. Without solid dataset, our evaluation score is misinterpreting the real score in production environment, and, therefore, proper datasets have essential role in research&development of any ML-based classifier or detector. The main motivation for this paper is to find a way how to evaluate quality of any dataset to estimate if it is good enough for ML experiments. To our best knowledge, there are only a few studies focused on quality evaluation of datasets with network traffic. For experiments, we selected datasets about DNS over HTTP (DoH) detection and URL classification problems that are already being elaborated. All metrics are calculated from dataset level. Impact of these metrics is evaluated on Random Forest (RF) model. We show results we have discovered in our datasets and ML detection modules. Eventually, we discuss possible next steps in this research.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 8th Prague Embedded Systems Workshop

ISBN

978-80-01-06772-7

ISSN

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e-ISSN

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Počet stran výsledku

2

Strana od-do

8-9

Název nakladatele

České vysoké učení technické v Praze

Místo vydání

Praha

Místo konání akce

Praha, virtual

Datum konání akce

6. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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