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Resolution Identification of Encrypted Video Streaming Based on HTTP/2 Features

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Published:06 February 2023Publication History
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Abstract

With the inevitable dominance of video traffic on the Internet, Internet service providers (ISP) are striving to deliver video streaming with high quality. Video resolution, as a direct reflection of video quality, is a key factor of the video quality of experience (QoE). Since the displayed information of video cannot be observed by ISPs, ISPs can only measure the video resolution from traffic. However, with HTTP/2 being gradually adopted in video services, the multiplexing feature of HTTP/2 allows audio and video chunks to be mixed during transmission, making existing monitoring approaches unusable. In this article, we propose a method called H2CI to monitor resolution for adaptive encrypted video traffic under HTTP/2. We consider the size of the mixed data for identification. Specifically, H2CI consists of a length restoration method to extract restored fingerprints and a fingerprint-matching method for fine-grained resolution identification. The experimental results show that H2CI can achieve more than 98% accuracy for fine-grained resolution identification. Our method can be effectively applied to infer the adaptation behavior of encrypted video streaming and monitor the QoE of video services under HTTP/2.

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REFERENCES

  1. [1] Akhshabi Saamer, Anantakrishnan Lakshmi, Begen Ali C., and Dovrolis Constantine. 2012. What happens when HTTP adaptive streaming players compete for bandwidth? In Proceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV’12). Association for Computing Machinery, New York, NY, 914. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Balachandran Athula, Sekar Vyas, Akella Aditya, Seshan Srinivasan, Stoica Ion, and Zhang Hui. 2012. A quest for an Internet video quality-of-experience metric. In Proceedings of the 11th ACM Workshop on Hot Topics in Networks. ACM, 97102. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Belshe Mike, Peon Roberto, and Thomson Martin. 2015. Hypertext transfer protocol version 2 (HTTP/2). RFC 7540 (2015), 196. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Bronzino Francesco, Schmitt Paul, Ayoubi Sara, Martins Guilherme, Teixeira Renata, and Feamster Nick. 2019. Inferring streaming video quality from encrypted traffic: Practical models and deployment experience. Proc. ACM Meas. Anal. Comput. Syst. 3, 3 (2019), 56:1–56:25. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Cai Xiang, Zhang Xin Cheng, Joshi Brijesh, and Johnson Rob. 2012. Touching from a distance: Website fingerprinting attacks and defenses. In Proceedings of the ACM Conference on Computer and Communications Security. ACM, 605616. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Cao Zigang, Xiong Gang, Zhao Yong, Li Zhenzhen, and Guo Li. 2014. A survey on encrypted traffic classification. In Applications and Techniques in Information Security. Springer, Berlin, 7381.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Celenk Özge, Bauschert Thomas, and Eckert Marcus. 2021. Machine learning based KPI monitoring of video streaming traffic for QoE estimation. SIGMETRICS Perform. Eval. Rev. 48, 4 (2021), 3336. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Cisco U. 2020. Cisco annual internet report (2018–2023) white paper. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf.Google ScholarGoogle Scholar
  9. [9] Dimopoulos Giorgos, Leontiadis Ilias, Barlet-Ros Pere, and Papagiannaki Konstantina. 2016. Measuring video QoE from encrypted traffic. In Proceedings of the ACM on Internet Measurement Conference. ACM, 513526. Retrieved from http://dl.acm.org/citation.cfm?id=2987459.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Ericsson A.. 2020. Ericsson mobility report. Ericsson: Stockholm, Sweden (2020). https://www.ericsson.com/49da93/assets/local/reports-papers/mobility-report/documents/2020/june2020-ericsson-mobility-report.pdf.Google ScholarGoogle Scholar
  11. [11] Gu Jiaxi, Wang Jiliang, Yu Zhiwen, and Shen Kele. 2018. Walls have ears: Traffic-based side-channel attack in video streaming. In Proceedings of the IEEE Conference on Computer Communications. IEEE, 15381546. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Gutterman Craig, Guo Katherine, Arora Sarthak, Wang Xiaoyang, Wu Les, Katz-Bassett Ethan, and Zussman Gil. 2019. Requet: Real-time QoE detection for encrypted YouTube traffic. In Proceedings of the 10th ACM Multimedia Systems Conference. ACM, 4859. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Hoßfeld Tobias, Seufert Michael, Hirth Matthias, Zinner Thomas, Tran-Gia Phuoc, and Schatz Raimund. 2011. Quantification of YouTube QoE via crowdsourcing. In Proceedings of the IEEE International Symposium on Multimedia. IEEE Computer Society, 494499. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Hoßfeld Tobias, Seufert Michael, Sieber Christian, and Zinner Thomas. 2014. Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming. In Proceedings of the 6th International Workshop on Quality of Multimedia Experience. IEEE, 111116. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Khokhar Muhammad Jawad, Ehlinger Thibaut, and Barakat Chadi. 2019. From network traffic measurements to QoE for internet video. In Proceedings of the IFIP Networking Conference. IEEE, 19. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Mangla Tarun, Halepovic Emir, Ammar Mostafa H., and Zegura Ellen W.. 2018. eMIMIC: Estimating HTTP-based video QoE metrics from encrypted network traffic. In Proceedings of the Network Traffic Measurement and Analysis Conference. IEEE, 18. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Mangla Tarun, Halepovic Emir, Ammar Mostafa H., and Zegura Ellen W.. 2019. Using session modeling to estimate HTTP-based video QoE metrics from encrypted network traffic. IEEE Trans. Netw. Serv. Manag. 16, 3 (2019), 10861099. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Mazhar M. Hammad and Shafiq Zubair. 2018. Real-time video quality of experience monitoring for HTTPS and QUIC. In Proceedings of the IEEE Conference on Computer Communications. IEEE, 13311339. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Mok Ricky K. P., Chan Edmond W. W., Luo Xiapu, and Chang Rocky K. C.. 2011. Inferring the QoE of HTTP video streaming from user-viewing activities. In Proceedings of the 1st ACM SIGCOMM Workshop on Measurements Up the Stack. ACM, 3136. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Nguyen Minh, Timmerer Christian, and Hellwagner Hermann. 2020. H2BR: An HTTP/2-based retransmission technique to improve the QoE of adaptive video streaming. In Proceedings of the 25th ACM Workshop on Packet Video. ACM, 17. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Orsolic Irena, Pevec Dario, Suznjevic Mirko, and Skorin-Kapov Lea. 2016. YouTube QoE estimation based on the analysis of encrypted network traffic using machine learning. In Proceedings of the IEEE Globecom Workshops, Washington. IEEE, 16. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Orsolic Irena, Suznjevic Mirko, and Skorin-Kapov Lea. 2018. YouTube QoE estimation from encrypted traffic: Comparison of test methodologies and machine learning based models. In Proceedings of the 10th International Conference on Quality of Multimedia Experience. IEEE, 16. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Papadogiannaki Eva and Ioannidis Sotiris. 2021. A survey on encrypted network traffic analysis applications, techniques, and countermeasures. ACM Comput. Surv. 54, 6 (2021), 123:1–123:35. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Reed Andrew and Klimkowski Benjamin. 2016. Leaky streams: Identifying variable bitrate DASH videos streamed over encrypted 802.11n connections. In Proceedings of the 13th IEEE Annual Consumer Communications & Networking Conference. IEEE, 11071112. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Reed Andrew and Kranch Michael J.. 2017. Identifying HTTPS-protected Netflix videos in real-time. In Proceedings of the 7th ACM Conference on Data and Application Security and Privacy. ACM, 361368. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Rescorla Eric. 2018. The transport layer security (TLS) protocol version 1.3. RFC 8446 (2018), 1160. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Schuster Roei, Shmatikov Vitaly, and Tromer Eran. 2017. Beauty and the burst: Remote identification of encrypted video streams. In Proceedings of the 26th USENIX Security Symposium. USENIX Association, 13571374. Retrieved from https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/schuster.Google ScholarGoogle Scholar
  28. [28] Seufert Michael, Casas Pedro, Wehner Nikolas, Gang Li, and Li Kuang. 2019. Features that matter: Feature selection for on-line stalling prediction in encrypted video streaming. In Proceedings of the IEEE Conference on Computer Communications Workshops. IEEE, 688695. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Seufert Michael, Egger Sebastian, Slanina Martin, Zinner Thomas, Hoßfeld Tobias, and Tran-Gia Phuoc. 2015. A survey on quality of experience of HTTP adaptive streaming. IEEE Commun. Surv. Tutor. 17, 1 (2015), 469492. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Shen Meng, Zhang Jinpeng, Xu Ke, Zhu Liehuang, Liu Jiangchuan, and Du Xiaojiang. 2020. DeepQoE: Real-time measurement of video QoE from encrypted traffic with deep learning. In Proceedings of the 28th IEEE/ACM International Symposium on Quality of Service. IEEE, 110. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Siekkinen Matti, Kämäräinen Teemu, Favario Leonardo, and Masala Enrico. 2018. Can you see what I see? Quality-of-experience measurements of mobile live video broadcasting. ACM Trans. Multim. Comput. Commun. Appl. 14, 2s (2018), 34:1–34:23. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Sodagar Iraj. 2011. The MPEG-DASH standard for multimedia streaming over the internet. IEEE Multim. 18, 4 (2011), 6267. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Spiteri Kevin, Sitaraman Ramesh K., and Sparacio Daniel. 2019. From theory to practice: Improving bitrate adaptation in the DASH reference player. ACM Trans. Multim. Comput. Commun. Appl. 15, 2s (2019), 67:1–67:29. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Tran Huyen T. T., Ngoc Nam Pham, Hoßfeld Tobias, Seufert Michael, and Thang Truong Cong. 2021. Cumulative quality modeling for HTTP adaptive streaming. ACM Trans. Multim. Comput. Commun. Appl. 17, 1 (2021), 22:1–22:24. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Ul-Mustafa Raza, Moura David, and Rothenberg Christian Esteve. 2021. Machine learning approach to estimate video QoE of encrypted DASH traffic in 5G networks. In Proceedings of the IEEE Statistical Signal Processing Workshop. IEEE, 586589. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Wassermann Sarah, Seufert Michael, Casas Pedro, Gang Li, and Li Kuang. 2019. I see what you see: Real time prediction of video quality from encrypted streaming traffic. In Proceedings of the 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks. ACM, 16. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Wassermann Sarah, Seufert Michael, Casas Pedro, Gang Li, and Li Kuang. 2019. Let me decrypt your beauty: Real-time prediction of video resolution and bitrate for encrypted video streaming. In Proceedings of the Network Traffic Measurement and Analysis Conference. IEEE, 199200. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Wassermann Sarah, Seufert Michael, Casas Pedro, Gang Li, and Li Kuang. 2020. ViCrypt to the rescue: Real-time, machine-learning-driven video-QoE monitoring for encrypted streaming traffic. IEEE Trans. Netw. Serv. Manag. 17, 4 (2020), 20072023. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Wu Hua, Cheng Guang, and Hu Xiaoyan. 2019. Inferring ADU combinations from encrypted QUIC stream. In Proceedings of the 14th International Conference on Future Internet Technologies. ACM, 4:1–4:6. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Wu Hua, Li Xin, Cheng Guang, and Hu Xiaoyan. 2021. Monitoring video resolution of adaptive encrypted video traffic based on HTTP/2 features. In Proceedings of the IEEE Conference on Computer Communications Workshops. IEEE, 16. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Xu Shichang, Sen Subhabrata, and Mao Z. Morley. 2020. CSI: Inferring mobile ABR video adaptation behavior under HTTPS and QUIC. In Proceedings of the 15th EuroSys Conference. ACM, 33:1–33:16. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. [42] Yahia Mariem Ben, Louédec Yannick Le, Nuaymi Loutfi, and Simon Gwendal. 2017. When HTTP/2 rescues DASH: Video frame multiplexing. In Proceedings of the IEEE Conference on Computer Communications Workshops. IEEE, 677682. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Yahia Mariem Ben, Louédec Yannick Le, Simon Gwendal, Nuaymi Loutfi, and Corbillon Xavier. 2019. HTTP/2-based frame discarding for low-latency adaptive video streaming. ACM Trans. Multim. Comput. Commun. Appl. 15, 1 (2019), 18:1–18:23. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Yang Luming, Fu Shaojing, Luo Yuchuan, and Shi Jiangyong. 2020. Markov probability fingerprints: A method for identifying encrypted video traffic. In Proceedings of the 16th International Conference on Mobility, Sensing and Networking. IEEE, 283–90. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Yarnagula Hema Kumar, Juluri Parikshit, Mehr Sheyda Kiani, Tamarapalli Venkatesh, and Medhi Deep. 2019. QoE for mobile clients with segment-aware rate adaptation algorithm (SARA) for DASH video streaming. ACM Trans. Multim. Comput. Commun. Appl. 15, 2 (2019), 36:1–36:23. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 2
        March 2023
        540 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3572860
        • Editor:
        • Abdulmotaleb El Saddik
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        • Published: 6 February 2023
        • Online AM: 28 July 2022
        • Accepted: 18 July 2022
        • Revised: 5 June 2022
        • Received: 30 January 2022
        Published in tomm Volume 19, Issue 2

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