Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133637" target="_blank" >RIV/63839172:_____/23:10133637 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/23:00363676

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-23633-4_13" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-23633-4_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-23633-4_13" target="_blank" >10.1007/978-3-031-23633-4_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA

Popis výsledku v původním jazyce

Machine learning is recognised as a relevant approach to detect attacks and other anomalies in network traffic. However, there are still no suitable network datasets that would enable effective detection. On the other hand, the preparation of a network dataset is not easy due to privacy reasons but also due to the lack of tools for assessing their quality. In a previous paper, we proposed a new method for data quality assessment based on permutation testing. This paper presents a parallel study on the limits of detection of such an approach. We focus on the problem of network flow classification and use well-known machine learning techniques. The experiments were performed using publicly available network datasets. (C) 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA

Popis výsledku anglicky

Machine learning is recognised as a relevant approach to detect attacks and other anomalies in network traffic. However, there are still no suitable network datasets that would enable effective detection. On the other hand, the preparation of a network dataset is not easy due to privacy reasons but also due to the lack of tools for assessing their quality. In a previous paper, we proposed a new method for data quality assessment based on permutation testing. This paper presents a parallel study on the limits of detection of such an approach. We focus on the problem of network flow classification and use well-known machine learning techniques. The experiments were performed using publicly available network datasets. (C) 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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

<a href="/cs/project/VJ02010024" target="_blank" >VJ02010024: Analýza šifrovaného provozu pomocí síťových toků</a><br>

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

ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases

ISBN

978-3-031-23632-7

ISSN

1865-0929

e-ISSN

—

Počet stran výsledku

16

Strana od-do

170-185

Název nakladatele

Springer

Místo vydání

Cham, Švýcarsko

Místo konání akce

Grenoble, Francie

Datum konání akce

19. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000967761200013