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Al Jameel, Mohammed, Turner, Scott, Kanakis, Triantafyllos, Al-Sherbaz, Ali 
ORCID: 0000-0002-0995-1262 and Bhaya, Wesam S
(2022)
Deep Learning Approach for Real-time Video Streaming Traffic Classification.
In:
2022 International Conference on Computer Science and Software Engineering (CSASE).
IEEE, pp. 168-174.
ISBN 9781665426329
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Text
11261-Al-Sherbaz-Deep-learning-approach-for- real-time-video-streaming-traffic-classification.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (7MB) | Preview |
Abstract
Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples’ lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3% when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach’s effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Traffic classification; Video streaming; Deep learning; Multi-layer feedforward Neural network |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Anne Pengelly |
| Date Deposited: | 29 Jun 2022 10:29 |
| Last Modified: | 31 Oct 2023 12:18 |
| URI: | https://eprints.glos.ac.uk/id/eprint/11261 |
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