Application Identification for Virtual Reality Video with Feature Analysis and Machine Learning Technique

  • Xiaoyu LiuEmail author
  • Xinyu Chen
  • Yumei Wang
  • Yu Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 262)


Immersive media services such as Virtual Reality (VR) video have attracted more and more attention in recent years. They are applications that typically require large bandwidth, low latency, and low packet loss ratio. With limited network resources in wireless network, video application identification is crucial for optimized network resource allocation, Quality of Service (QoS) assurance, and security management. In this paper, we propose a set of statistical features that can be used to distinguish VR video from ordinary video. Six supervised machine learning (ML) algorithms are explored to verify the identification performance for VR video application using these features. Experimental results indicate that the proposed features combined with C4.5 Decision Tree algorithm can achieve an accuracy of 98.6% for VR video application identification. In addition, considering the requirement of real-time traffic identification, we further make two improvements to the statistical features and training set. One is the feature selection algorithm to improve the computational performance, and the other is the study of the overall accuracy in respect to training set size to obtain the minimum training set size.


Application identification Statistical feature Machine learning VR video application 



This work has been sponsored by Huawei Research Fund (grant No. YBN2016110032) and National Science Foundation of China (No. 61201149). The authors would also like to thank the reviewers for their constructive comments.


  1. 1.
  2. 2.
    Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: multilevel traffic classification in the dark. In: ACM SIGCOMM Computer Communication Review, Pennsylvania, pp. 229–240 (2005)CrossRefGoogle Scholar
  3. 3.
    Kim, H., Claffy, K.C., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: Internet traffic classification demystified: myths, caveats, and the best practices. In: Proceedings of the 2008 ACM CoNEXT Conference, Spain, p. 11 (2008)Google Scholar
  4. 4.
    Andersson, R.: Classification of video traffic: an evaluation of video traffic classification using random forests and gradient boosted trees. Digitala Vetenskapliga Arkivet. 83 (2017)Google Scholar
  5. 5.
    Västlund, F.: Video flow classification: a runtime performance study. Digitala Vetenskapliga Arkivet. 68 (2017)Google Scholar
  6. 6.
    Moore, A., Zuev, D.,Crogan, M.: Discriminators for use in flow-based classification. (2013)Google Scholar
  7. 7.
    Yamansavascilar, B., Guvensan, M. A., Yavuz, A. G., Karsligil, M. E.: Application identification via network traffic classification. In: International Conference on ICNC, Santa Clara, pp. 843–848(2017)Google Scholar
  8. 8.
    Aggarwal, R., Singh, N.: A new hybrid approach for network traffic classification using SVM and Naïve Bayes algorithm. Int. J. Comput. Sci. Mobile Comput. 6, 168–174 (2017)Google Scholar
  9. 9.
    Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)CrossRefGoogle Scholar
  10. 10.
    Williams, N., Zander, S.: Evaluating machine learning algorithms for automated network application identification. (2006)Google Scholar
  11. 11.
    Chen, Z., Chen, R., Zhang, Y., Zhang, J., Xu, J.: A Statistical-Feature ML Approach to IP Traffic Classification Based on CUDA. In: IEEE Trustcom/BigDataSE/ISPA, Tianjin, pp. 2235–2239 (2017)Google Scholar
  12. 12.
    Datta, J., Kataria, N., Hubballi, N.: Network traffic classification in encrypted environment: a case study of google hangout. In: Twenty First National Conference on Communications (NCC), pp. 1–6 Mumbai, India (2015)Google Scholar
  13. 13.
    Munther, A., Alalousi, A., Nizam, S., Othman, R. R., Anbar, M.: Network traffic classification-A comparative study of two common decision tree methods: C4.5 and Random forest. In: International Conference on Electronic Design, pp. 210–214 Penang (2014)Google Scholar
  14. 14.
    Wang, C., Xu, T., Qin, X.: Network traffic classification with improved random forest. In: International Conference on Computational Intelligence and Security, pp. 78–81 Shenzhen (2015)Google Scholar
  15. 15.
    Wireshark, Accessed 22 May 2018
  16. 16.
    WEKA: Data Mining Software in Java. Accessed 26 May 2018

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

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