Deep learning approach for real-time video streaming traffic classification

Conference paper


Turner, S., Al Jameel, M., Kanakis, T., Al-Sherbaz, A. and Bhaya, W. 2022. Deep learning approach for real-time video streaming traffic classification.
AuthorsTurner, S., Al Jameel, M., Kanakis, T., Al-Sherbaz, A. and Bhaya, W.
TypeConference paper
Description

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. 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.

KeywordsTraffic classification; Video streaming; Deep Learning; Neural network
Year2022
Conference2022 International Conference on Computer Science and Software Engineering CSASE, Duhok, Kurdistan Region – Iraq
Official URLhttps://csase.uod.ac/#home
File
File Access Level
Open
References

[1] T. Barnett, S. Jain, U. Andra, and T. Khurana, “Cisco visual networking
index (vni), complete forecast update, 2017–2022,” Americas/EMEAR
Cisco Knowledge Network (CKN) Presentation, 2018.
[2] K. L. Dias, M. A. Pongelupe, W. M. Caminhas, and L. de Errico,
“An innovative approach for real-time network traffic classification,”
Computer Networks, vol. 158, pp. 143–157, 2019.
[3] A. Ellis and M. Sorokina, Optical Communication Systems: Limits and
Possibilities. CRC Press, 2019.
[4] X. Huang, T. Yuan, G. Qiao, and Y. Ren, “Deep reinforcement learning
for multimedia traffic control in software defined networking,” IEEE
Network, vol. 32, no. 6, pp. 35–41, 2018.
[5] J. Frnda, M. Voznak, and L. Sevcik, “Impact of packet loss and delay
variation on the quality of real-time video streaming,” Telecommunication
Systems, vol. 62, no. 2, pp. 265–275, 2016.
[6] N. Carlsson, D. Eager, V. Krishnamoorthi, and T. Polishchuk, “Optimized
adaptive streaming of multi-video stream bundles,” IEEE transactions
on multimedia, vol. 19, no. 7, pp. 1637–1653, 2017.
[7] P. Tang, Y. Dong, J. Jin, and S. Mao, “Fine-grained classification of
internet video traffic from qos perspective using fractal spectrum,” IEEE
Transactions on Multimedia, 2019.
[8] F. Audah, T. S. Chin, R. Kapsin, N. Omar, and A. Tajuddin, “Future
direction of traffic classification in sdn from current patents point-ofview,”
in 2019 15th International Computer Engineering Conference
(ICENCO). IEEE, 2019, pp. 121–125.
[9] E. Biersack, C. Callegari, M. Matijasevic et al., “Data traffic monitoring
and analysis,” Lecture Notes in Computer Science, vol. 5, no. 23, pp.
12 561–12 570, 2013.
[10] A. Canovas, J. M. Jimenez, O. Romero, and J. Lloret, “Multimedia
data flow traffic classification using intelligent models based on traffic
patterns,” IEEE Network, vol. 32, no. 6, pp. 100–107, 2018.
[11] “Cisco Annual Internet Report (2018–2023),” Cisco, 2020. [Online].
Available: https://www.cisco.com/c/en/us/solutions/collateral/executiveperspect...
annual-internet-report/white-paper-c11-741490.pdf
[12] A. Rao, A. Legout, Y.-s. Lim, D. Towsley, C. Barakat, and W. Dabbous,
“Network characteristics of video streaming traffic,” in Proceedings
of the Seventh COnference on emerging Networking EXperiments and
Technologies, 2011, pp. 1–12.
[13] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss, “An
architecture for differentiated services,” 1998.
[14] L. AlSuwaidan, “Data management model for internet of everything,”
in International Conference on Mobile Web and Intelligent Information
Systems. Springer, 2019, pp. 331–341.
[15] H. A. H. Ibrahim, O. R. A. Al Zuobi, M. A. Al-Namari, G. MohamedAli,
and A. A. A. Abdalla, “Internet traffic classification using machine
learning approach: Datasets validation issues,” in 2016 Conference of
Basic Sciences and Engineering Studies (SGCAC). IEEE, 2016, pp.
158–166.
[16] W. Zai-jian, Y.-n. Dong, H.-x. Shi, Y. Lingyun, and T. Pingping, “Internet
video traffic classification using qos features,” in 2016 International
Conference on Computing, Networking and Communications (ICNC).
IEEE, 2016, pp. 1–5.
[17] T. Bakhshi and B. Ghita, “On internet traffic classification: A twophased
machine learning approach,” Journal of Computer Networks and
Communications, vol. 2016, 2016.
[18] Y.-n. Dong, J.-j. Zhao, and J. Jin, “Novel feature selection and classification
of internet video traffic based on a hierarchical scheme,” Computer
Networks, vol. 119, pp. 102–111, 2017.
[19] A. Shaout and B. Crispin, “Streaming video classification using machine
learning,” The International Arab Journal of Information Technology,
vol. 17, no. 4A, pp. 677–682, 2020.
[20] Y. Miao, Z. Ruan, L. Pan, J. Zhang, and Y. Xiang, “Comprehensive analysis
of network traffic data,” Concurrency and Computation: Practice
and Experience, vol. 30, no. 5, p. e4181, 2018.
[21] J. Zhang, Y. Xiang, Y. Wang, W. Zhou, Y. Xiang, and Y. Guan, “Network
traffic classification using correlation information,” IEEE Transactions
on Parallel and Distributed systems, vol. 24, no. 1, pp. 104–117, 2012.
[22] L.-Y. Yang, Y.-N. Dong, W. Tian, and Z.-J. Wang, “The study of
new features for video traffic classification,” Multimedia Tools and
Applications, vol. 78, no. 12, pp. 15 839–15 859, 2019.
[23] L.-H. Chang, T.-H. Lee, H.-C. Chu, and C. Su, “Application-based online
traffic classification with deep learning models on sdn networks,” Adv.
Technol. Innov, vol. 5, pp. 216–229, 2020.
[24] R. Rendall, I. Castillo, A. Schmidt, S.-T. Chin, L. H. Chiang, and
M. Reis, “Wide spectrum feature selection (wise) for regression model
building,” Computers & Chemical Engineering, vol. 121, pp. 99–110,
2019.
[25] J. Hauke and T. Kossowski, “Comparison of values of pearson’s and
spearman’s correlation coefficients on the same sets of data,” Quaestiones
geographicae, vol. 30, no. 2, pp. 87–93, 2011.
[26] M. Al Jameel, “Deep learning approach for real-time
video streaming traffic classification,” 2021. [Online]. Available:
https://github.com/mo7ammedfadhil/Video-streaming-dataset
[27] N. Namdev, S. Agrawal, and S. Silkari, “Recent advancement in machine
learning based internet traffic classification,” Procedia Computer
Science, vol. 60, pp. 784–791, 2015.
[28] M. R. Parsaei, M. J. Sobouti, S. R. Khayami, and R. Javidan, “Network
traffic classification using machine learning techniques over software
defined networks,” International Journal of Advanced Computer Science
and Applications, vol. 8, no. 7, pp. 220–225, 2017.
[29] A. Wuraola and N. Patel, “Sqnl: A new computationally efficient
activation function,” in 2018 International Joint Conference on Neural
Networks (IJCNN). IEEE, 2018, pp. 1–7.
[30] C. Sammut and G. I.Webb, Encyclopedia of machine learning. Springer
Science & Business Media, 2011.

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Deposited07 Mar 2022
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