A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/515 |
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| author | Tianhua Chen Elans Grabs Aleksandrs Ipatovs Maria-Dolores Cano |
| author_facet | Tianhua Chen Elans Grabs Aleksandrs Ipatovs Maria-Dolores Cano |
| author_sort | Tianhua Chen |
| collection | DOAJ |
| description | Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic. |
| format | Article |
| id | doaj-art-1b4824a099cc4d81ac338a2525d031d0 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1b4824a099cc4d81ac338a2525d031d02025-01-24T13:19:39ZengMDPI AGApplied Sciences2076-34172025-01-0115251510.3390/app15020515A Novel Proprietary Internet Video Traffic Dataset Generation AlgorithmTianhua Chen0Elans Grabs1Aleksandrs Ipatovs2Maria-Dolores Cano3Institute of Photonics, Electronics and Telecommunications, Riga Technical University, LV-1048 Riga, LatviaInstitute of Photonics, Electronics and Telecommunications, Riga Technical University, LV-1048 Riga, LatviaInstitute of Photonics, Electronics and Telecommunications, Riga Technical University, LV-1048 Riga, LatviaDepartment of Information Technologies and Communication, Universidad Politécnica de Cartagena, Plaza del Hospital 1, 30202 Cartagena, SpainConsidering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.https://www.mdpi.com/2076-3417/15/2/515network traffic classificationproprietary datasetalgorithminterpretability |
| spellingShingle | Tianhua Chen Elans Grabs Aleksandrs Ipatovs Maria-Dolores Cano A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm Applied Sciences network traffic classification proprietary dataset algorithm interpretability |
| title | A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm |
| title_full | A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm |
| title_fullStr | A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm |
| title_full_unstemmed | A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm |
| title_short | A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm |
| title_sort | novel proprietary internet video traffic dataset generation algorithm |
| topic | network traffic classification proprietary dataset algorithm interpretability |
| url | https://www.mdpi.com/2076-3417/15/2/515 |
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