Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21240/23:00363676
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA
Original language description
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.
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
<a href="/en/project/VJ02010024" target="_blank" >VJ02010024: Flow-based Encrypted Traffic Analysis</a><br>
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
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
—
Number of pages
16
Pages from-to
170-185
Publisher name
Springer
Place of publication
Cham, Švýcarsko
Event location
Grenoble, Francie
Event date
Sep 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000967761200013