Accurate Identification of Internet Video Traffic Using Byte Code Distribution Features

  • Yuxi Xie
  • Hanbo Deng
  • Lizhi PengEmail author
  • Zhenxiang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)


Video traffic, the most rapidly growing traffic type in Internet, is posing a serious challenge to Internet management. Different kinds of Internet video contents, including illegal and adult contents, make it necessary to manage different video traffic using different strategies. Unfortunately, there are few research work concerning Internet video traffic type identification. In this paper, we propose a new effective feature extraction method, namely byte code distribution (BCD), for Internet video traffic type identification. The BCD method first counts the times of each byte code value (0 to 255) from a video flow, and then computes the ratio between each count and the total byte count. Such that the 256 ratios are used as the features. Comparing with traditional packet-level features, the BCD features contain more video type information, and are able to make identification more accurately. To test the performance of our proposal, we collect a set of video traffic traces containing two typical video types, romance and action. We conduct a set of comparing experiments on the collected data. The results show that the BCD method can hit extremely high identification accuracies (higher than 99%), far higher than those of the traditional packet-level feature extracting methods. The empirical studies show that the BCD method is promising for Internet video traffic identification.


Byte code distribution Feature extraction Video traffic identification Machine learning 



This research was partially supported by the National Natural Science Foundation of China under grant No. 61472164, No. 61573166, No. 61572230, and No. 61672262, the Doctoral Fund of University of Jinan under grant No. XBS1623, and No. XBS1523.


  1. 1.
  2. 2.
    Chaisorn, L., Fu, Z.: A hybrid approach for image/video content representation and identification. In: Industrial Electronics and Applications, pp. 966–971 (2012)Google Scholar
  3. 3.
    Dong, S., Li, R.: Traffic identification method based on multiple probabilistic neural network model. Neural Comput. Appl. 1, 1–15 (2017)Google Scholar
  4. 4.
    Dong, Y.N., Zhao, J.J., Jin, J.: Novel feature selection and classification of internet video traffic based on a hierarchical scheme. Comput. Netw. 119, 102–111 (2017)CrossRefGoogle Scholar
  5. 5.
    Ewerth, R., Mühling, M., Freisleben, B.: Robust video content analysis via transductive learning. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 41 (2012)Google Scholar
  6. 6.
    Gong, J., Wang, W., Wang, P., Sun, Z.: P2P traffic identification method based on an improvement incremental SVM learning algorithm. In: International Symposium on Wireless Personal Multimedia Communications, pp. 174–179 (2015)Google Scholar
  7. 7.
    Li, Y.N., Chen, X.P.: Robust and compact video descriptor learned by deep neural network. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2162–2166 (2017)Google Scholar
  8. 8.
    Liu, Y., Sadeghi, A.R., Ghosal, D., Mukherjee, B.: Video streaming forensic-content identification with traffic snooping. Asian J. Agric. Rural Dev. 2(10), 39–45 (2010)Google Scholar
  9. 9.
    Moore, A.W., Zuev, D.: Discriminators for Use in Flow-Based Classification. Intel Research, London (2005)Google Scholar
  10. 10.
    Mu, X., Wu, W.: A parallelized network traffic classification based on hidden markov model. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 107–112 (2011)Google Scholar
  11. 11.
    Peng, L., Xue, Y., Wang, C.: Survey on recognition and filtering of network video content. Comput. Eng. Des. 10, 048 (2008)CrossRefGoogle Scholar
  12. 12.
    Peng, L., Yang, B., Chen, Y., Chen, Z.: Effectiveness of statistical features for early stage internet traffic identification. Int. J. Parallel Program. 44(1), 181–197 (2016)CrossRefGoogle Scholar
  13. 13.
    Peng, L., Zhang, H., Chen, Y., Yang, B.: Imbalanced traffic identification using an imbalanced data gravitation-based classification model. Comput. Commun. 102(1), 177–189 (2017)CrossRefGoogle Scholar
  14. 14.
    Qiao, M., Ma, Y., Bian, Y., Liu, J.: Real-time multi-application network traffic identification based on machine learning. In: International Symposium on Neural Networks, pp. 473–480 (2015)Google Scholar
  15. 15.
    Rao, Z., Niu, W., Zhang, X., Li, H.: Tor anonymous traffic identification based on gravitational clustering. Peer-to-Peer Netw. Appl. 11(3), 592–601 (2018)CrossRefGoogle Scholar
  16. 16.
    Rasheed, Z., Shah, M.: Movie genre classification by exploiting audio-visual features of previews. In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 1086–1089 (2002)Google Scholar
  17. 17.
    Schuster, R., Shmatikov, V., Tromer, E.: Beauty and the burst: remote identification of encrypted video streams. In: Proceedings of the 26th USENIX Security Symposium, pp. 1357–1374 (2017)Google Scholar
  18. 18.
    Shinkar, T., Hanchate, D.B.: Video content identification using video signature: survey. Int. Res. J. Eng. Technol. (IRJET) 4, 746–751 (2017)Google Scholar
  19. 19.
    Ye, Z., Wang, M., Wang, C., Xu, H.: P2P traffic identification using support vector machine and cuckoo search algorithm combined with particle swarm optimization algorithm. In: Zhang, S., Xu, K., Xu, M., Wu, J., Wu, C., Zhong, Y. (eds.) ICoC 2014. CCIS, vol. 502, pp. 118–132. Springer, Heidelberg (2015). Scholar
  20. 20.
    Yuan, X., Lai, W., Mei, T., Hua, X.S., Wu, X.Q., Li, S.: Automatic video genre categorization using hierarchical SVM. In: IEEE International Conference on Image Processing, pp. 2905–2908 (2006)Google Scholar
  21. 21.
    Zhang, J., Chen, C., Xiang, Y., Zhou, W., Vasilakos, A.V.: An effective network traffic classification method with unknown flow detection. IEEE Trans. Netw. Serv. Manage. 10(2), 133–147 (2013)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Chen, X., Xiang, Y., Zhou, W., Wu, J.: Robust network traffic classification. IEEE/ACM Trans. Netw. 23(4), 1257–1270 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yuxi Xie
    • 1
  • Hanbo Deng
    • 1
  • Lizhi Peng
    • 1
    Email author
  • Zhenxiang Chen
    • 1
  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanPeople’s Republic of China

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