CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines
Abstract The modern approach for network traffic classification (TC), which is an important part of operating and securing networks, is to use machine learning (ML) models that are able to learn intricate relationships between traffic characteristics and communicating applications. A crucial prerequ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03927-4 |
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| _version_ | 1846172221943316480 |
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| author | Karel Hynek Jan Luxemburk Jaroslav Pešek Tomáš Čejka Pavel Šiška |
| author_facet | Karel Hynek Jan Luxemburk Jaroslav Pešek Tomáš Čejka Pavel Šiška |
| author_sort | Karel Hynek |
| collection | DOAJ |
| description | Abstract The modern approach for network traffic classification (TC), which is an important part of operating and securing networks, is to use machine learning (ML) models that are able to learn intricate relationships between traffic characteristics and communicating applications. A crucial prerequisite is having representative datasets. However, datasets collected from real production networks are not being published in sufficient numbers. Thus, this paper presents a novel dataset, CESNET-TLS-Year22, that captures the evolution of TLS traffic in an ISP network over a year. The dataset contains 180 web service labels and standard TC features, such as packet sequences. The unique year-long time span enables comprehensive evaluation of TC models and assessment of their robustness in the face of the ever-changing environment of production networks. |
| format | Article |
| id | doaj-art-15ccc647721747d8a54b8a0463d24db0 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-15ccc647721747d8a54b8a0463d24db02024-11-10T12:06:18ZengNature PortfolioScientific Data2052-44632024-10-0111111010.1038/s41597-024-03927-4CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone linesKarel Hynek0Jan Luxemburk1Jaroslav Pešek2Tomáš Čejka3Pavel Šiška4CESNETCESNETCESNETCESNETCESNETAbstract The modern approach for network traffic classification (TC), which is an important part of operating and securing networks, is to use machine learning (ML) models that are able to learn intricate relationships between traffic characteristics and communicating applications. A crucial prerequisite is having representative datasets. However, datasets collected from real production networks are not being published in sufficient numbers. Thus, this paper presents a novel dataset, CESNET-TLS-Year22, that captures the evolution of TLS traffic in an ISP network over a year. The dataset contains 180 web service labels and standard TC features, such as packet sequences. The unique year-long time span enables comprehensive evaluation of TC models and assessment of their robustness in the face of the ever-changing environment of production networks.https://doi.org/10.1038/s41597-024-03927-4 |
| spellingShingle | Karel Hynek Jan Luxemburk Jaroslav Pešek Tomáš Čejka Pavel Šiška CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines Scientific Data |
| title | CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines |
| title_full | CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines |
| title_fullStr | CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines |
| title_full_unstemmed | CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines |
| title_short | CESNET-TLS-Year22: A year-spanning TLS network traffic dataset from backbone lines |
| title_sort | cesnet tls year22 a year spanning tls network traffic dataset from backbone lines |
| url | https://doi.org/10.1038/s41597-024-03927-4 |
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