Network Traffic Classification Based on Single Flow Time Series Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133607" target="_blank" >RIV/63839172:_____/23:10133607 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/23:00369772
Result on the web
<a href="https://ieeexplore.ieee.org/document/10327876" target="_blank" >https://ieeexplore.ieee.org/document/10327876</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/CNSM59352.2023.10327876" target="_blank" >10.23919/CNSM59352.2023.10327876</a>

Result language
angličtina
Original language name
Network Traffic Classification Based on Single Flow Time Series Analysis
Original language description
Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication. Nevertheless, the packet aggregation into flow records naturally causes information loss; therefore, this paper proposes a novel flow extension for traffic features based on the time series analysis of the Single Flow Time series, i.e., a time series created by the number of bytes in each packet and its timestamp. We propose 69 universal features based on the statistical analysis of data points, time domain analysis, packet distribution within the flow timespan, time series behavior, and frequency domain analysis. We have demonstrated the usability and universality of the proposed feature vector for various network traffic classification tasks using 15 well-known publicly available datasets. Our evaluation shows that the novel feature vector achieves classification performance similar or better than related works on both binary and multiclass classification tasks. In more than half of the evaluated tasks, the classification performance increased by up to 5 %.
Czech name
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Czech description
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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)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
19th International Conference on Network and Service Management, CNSM 2023
ISBN
978-3-903176-59-1
ISSN
2165-963X
e-ISSN
—
Number of pages
7
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway , USA
Event location
Niagara Falls, Kanada
Event date
Oct 30, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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