Skip to main content
SpringerLink
Log in

ENCVIDC: an innovative approach for encoded video content classification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the increase in the sum of online video viewers on the internet every day, the video service providers are getting interested to know about the nature of the content being viewed through the supplied network in order to accomplish their business associated objectives that may include the user’s internet behavior profile, etc. Due to the widespread use of encoded video streaming techniques, the network video traffic classification has turned out to be a challenging task. As devoid of the authentic decryption key, it is impossible to comprehend the actual content viewed by the user. However, the current advances in machine learning have demonstrated the fact that encryption can also lead to certain information leak which yields promising results in determining the actual transmitted content between the two communicating parties. This research proposes a classifier for determining the encrypted video content over different streaming sites such as YouTube, Netflix and Dailymotion. We demonstrated that an eavesdropper can determine the stream video content even if the traffic is encrypted by identifiable patterns extracted from the captured traffic. We used different machine algorithms for the task and conducted a series of tests, demonstrating that our classification based on Random Forest showed accuracy greater than 98% and has the ability to execute all the network-related business objectives of any enterprise network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Soni M, Rajput BS (2021) Security and Performance Evaluations of QUIC Protocol. In Data Science and Intelligent Applications, Springer, Singapore, pp 457–462

    Google Scholar 

  2. Gärdborn P (2020) Is QUIC a Better Choice than TCP in the 5G Core Network Service Based Architecture?

  3. Nalawade O, Dhanwani A, Prabhu T (2018) Comparison of Present-day Transport Layer network Protocols and Google’s QUIC. In: 2018 International Conference on Smart City and Emerging Technology, (pp. 1-8). IEEE

  4. Akbari I, Salahuddin MA, Ven L, Limam N, Boutaba R, Mathieu B, Tuffin S (2021) A look behind the curtain: traffic classification in an increasingly encrypted web. Proc ACM Meas Anal Comput Syst 5(1):1–26

    Article  Google Scholar 

  5. Shi C, Bhargava B (1998, October) An efficient MPEG video encryption algorithm. In: Proceedings seventeenth IEEE symposium on reliable distributed systems, (pp. 381–386). IEEE

  6. Stockhammer T (2011, February) Dynamic adaptive streaming over HTTP– standards and design principles. In: Proceedings of the second annual ACM conference on Multimedia systems, (pp. 133–144)

  7. Sodagar I (2011) The mpeg-dash standard for multimedia streaming over the internet. IEEE multimedia 18(4):62–67

    Article  Google Scholar 

  8. Gu J, Wang J, Yu Z, Shen K (2018, April) Walls have ears: Traffic-based side-channel attack in video streaming. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, (pp. 1538–1546). IEEE

  9. Li F, Chung J W, Claypool M (2018, June) Silhouette: Identifying youtube video flows from encrypted traffic. In: Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, (pp. 19–24)

  10. Andersson R (2017) Classification of video traffic: an evaluation of video traffic classification using random forests and gradient boosted trees

  11. Liu Y, Li S, Zhang C, Zheng C, Sun Y, Liu Q (2020, June) Itp-knn: Encrypted video flow identification based on the intermittent traffic pattern of video and k-nearest neighbors classification. In International Conference on Computational Science, (pp. 279–293). Springer, Cham

  12. Rasteh A, Delpech F, Aguilar-Melchor C, Zimmer R, Shouraki S B, Masquelier T (2021) Encrypted Internet traffic classification using a supervised Spiking Neural Network. arXiv preprint arXiv:2101.09818

  13. Wassermann S, Seufert M, Casas P, Gang L, Li K (2019, June) Let me decrypt your beauty: Real-time prediction of video resolution and bitrate for encrypted video streaming. In: 2019 Network Traffic Measurement and Analysis Conference, (TMA) (pp. 199–200). IEEE

  14. Seufert M, Wassermann S, Casas P (2019) Considering user behavior in the quality of experience cycle: towards proactive QoE-aware traffic management. IEEE Commun Lett 23(7):1145–1148

    Article  Google Scholar 

  15. Kumar PA, Chandramathi S (2015) Intelligent video QoE prediction model for error-prone networks. Indian J Sci Technol 8(16):1

    Google Scholar 

  16. Morshedi M, Noll J (2021) Estimating PQoS of video streaming on Wi-Fi networks using machine learning. Sensors 21(2):621

    Article  Google Scholar 

  17. Shi Y, Ross A, Biswas S (2018) Source identification of encrypted video traffic in the presence of heterogeneous network traffic. Comput Commun 129:101–110

    Article  Google Scholar 

  18. Shi Y, Feng D, Cheng Y, Biswas S (2021) A natural language-inspired multilabel video streaming source identification method based on deep neural networks. Signal, Image and Video Processing, pp 1–8

    Google Scholar 

  19. Michie D, Spiegelhalter DJ, Taylor C C (1994) Machine learning, neural and statistical classification

  20. Burkart N, Huber MF (2021) A survey on the explainability of supervised machine learning. J Artif Intell Res 70:245–317

    Article  MathSciNet  Google Scholar 

  21. Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng 160(1):3–24

    Google Scholar 

  22. Osisanwo FY, Akinsola JET, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J (2017) Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol (IJCTT) 48(3):128–138

    Article  Google Scholar 

  23. Papadamou K, Papasavva A, Zannettou S, Blackburn J, Kourtellis N, Leontiadis I, Sirivianos M (2020, May) Disturbed YouTube for kids: Characterizing and detecting inappropriate videos targeting young children. In: Proceedings of the international AAAI conference on web and social media, (Vol. 14, pp. 522–533)

  24. Vishwakarma G, Thakur GS (2019) Comparative performance analysis of combined SVM-PCA for content-based video classification by utilizing inception V3. Int J Emerg Technol 10(3):397–403

    Google Scholar 

  25. Kandakatla R (2016) Identifying offensive videos on YouTube

  26. Dubin R, Dvir A, Hadar O, Pele O (2016) I know what you saw last minute-the chrome browser case. Black Hat Europe

  27. Dubin R, Dvir A, Pele O, Hadar O (2017) I know what you saw last minute-encrypted http adaptive video streaming title classification. IEEE Trans Inf Forensics Secur 12(12):3039–3049

    Article  Google Scholar 

  28. Schuster R, Shmatikov V, Tromer E (2017) Beauty and the burst: Remote identification of encrypted video streams. In: 26th USENIX Security Symposium, (pp. 1357–1374)

  29. O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458

  30. Reed A, Kranch M (2017, March) Identifying https-protected netflix videos in real-time. In: Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, (pp. 361–368)

  31. Li Y, Huang Y, Xu R, Seneviratne S, Thilakarathna K, Cheng A, ... Jourjon G (2018, November) Deep content: unveiling video streaming content from encrypted WiFi traffic. In: 2018 IEEE 17th International Symposium on Network Computing and Applications, (pp. 1–8). IEEE

  32. Dvir A, Marnerides A K, Dubin R, Golan N (2019, February) Clustering the Unknown-The Youtube Case. In: 2019 International Conference on Computing, Networking and Communications, (pp. 402–407). IEEE

  33. Ding C, He X (2004, July) K-means clustering via principal component analysis. In: Proceedings of the twenty-first international conference on Machine learning, (p. 29)

  34. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Inf Assoc 18(5):544–551

    Article  Google Scholar 

  35. Cincotta Pablo M, Giordano Claudia M, Silva Raphael Alves, Beaugé Cristián (2021) The Shannon entropy: an efficient indicator of dynamical stability. Phys D: Nonlinear Phenom 417:132816

    Article  MathSciNet  Google Scholar 

  36. Catt E, Norrish M (2021, January) On the formalisation of Kolmogorov complexity. In: Proceedings of the 10th ACM SIGPLAN International Conference on Certified Programs and Proofs, (pp. 291–299)

  37. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1–2):245–271

    Article  MathSciNet  Google Scholar 

  38. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E (2011) Scikit-learn: machine learning in python. J mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  39. Tebaa M, El Hajji S, El Ghazi A (2012, April) Homomorphic encryption method applied to Cloud Computing. In: 2012 National Days of Network Security and Systems, (pp. 86–89). IEEE

  40. Xin D, Ji J, Jing F, Gao M. Xue B (2021) Efficient Fully homomorphic encryption scheme using Ring-LWE. In: Journal of Physics: Conference Series, (Vol. 1738, No. 1, p. 012105). IOP Publishing

Download references

Author information

Authors and Affiliations

  1. National University of Sciences and Technology, Islamabad, 44000, Pakistan

    Faiqa Amjad, Fawad Khan, Shahzaib Tahir, Tahreem Yaqoob & Haider Abbas

Authors
  1. Faiqa Amjad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fawad Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahzaib Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tahreem Yaqoob
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haider Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haider Abbas.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amjad, F., Khan, F., Tahir, S. et al. ENCVIDC: an innovative approach for encoded video content classification. Neural Comput & Applic 34, 18685–18702 (2022). https://doi.org/10.1007/s00521-022-07480-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07480-2

Keywords

Search

Navigation