Deep learning approach for real-time video streaming traffic classification

 

Title

Deep learning approach for real-time video streaming traffic classification

Authors

Turner, S., Al Jameel, M., Kanakis, T., Al-Sherbaz, A. and Bhaya, W.

Conference

2022 International Conference on Computer Science and Software Engineering CSASE, Duhok, Kurdistan Region – Iraq

Official URL

https://csase.uod.ac/#home

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.

To read the full paper go to: https://researchspace.canterbury.ac.uk/90950/deep-learning-approach-for-real- 

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. https://doi.org/10.1016/j.comnet.2019.04.004   [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. https://doi.org/10.1109/MNET.2018.1800097   [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. https://doi.org/10.1007/s11235-015-0037-2   [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. https://doi.org/10.1109/TMM.2017.2673412   [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. https://doi.org/10.1109/MNET.2018.1800121   [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. https://doi.org/10.1016/S1359-6128(12)70041-1   [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. https://doi.org/10.1016/j.comnet.2017.03.019   [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. https://doi.org/10.34028/iajit/17/4A/13   [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. https://doi.org/10.1002/cpe.4181   [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. https://doi.org/10.1109/TPDS.2012.98   [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. https://doi.org/10.1007/s40274-019-6296-5   [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. https://doi.org/10.1038/s41415-020-2089-2 PMid:32855468   [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. https://doi.org/10.2478/v10117-011-0021-1   [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. https://doi.org/10.1016/j.procs.2015.08.238   [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. https://doi.org/10.1016/j.topol.2017.04.014   [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.