Page 482 - 《软件学报》2025年第7期
P. 482
吴桦 等: 面向 HTTP/2 流量多路复用特征的加密视频识别方法 3403
Multimedia Tools and Applications, 2018, 77(6): 7383–7404. [doi: 10.1007/s11042-017-4642-9]
[8] Yang LM, Fu SJ, Luo YC, Shi JY. Markov probability fingerprints: A method for identifying encrypted video traffic. In: Proc. of the 16th
Int’l Conf. on Mobility, Sensing and Networking (MSN). Tokyo: IEEE, 2020. 283–290. [doi: 10.1109/MSN50589.2020.00055]
[9] Li F, Chung JW, Claypool M. Silhouette: Identifying YouTube video flows from encrypted traffic. In: Proc. of the 28th ACM SIGMM
Workshop on Network and Operating Systems Support for Digital Audio and Video. Amsterdam: ACM, 2018. 19–24. [doi: 10.1145/
3210445.3210448]
[10] Shi Y, Biswas S. A deep-learning enabled traffic analysis engine for video source identification. In: Proc. of the 11th Int’l Conf. on
Communication Systems & Networks (COMSNETS). Bengaluru: IEEE, 2019. 15–21. [doi: 10.1109/COMSNETS.2019.8711478]
[11] Shi Y, Feng DZ, Cheng Y, Biswas S. A natural language-inspired multilabel video streaming source identification method based on deep
neural networks. Signal, Image and Video Processing, 2021, 15(6): 1161–1168. [doi: 10.1007/s11760-020-01844-8]
[12] Kattadige C, Raman A, Thilakarathna K, Lutu A, Perino D. 360NorVic: 360-degree video classification from mobile encrypted video
traffic. In: Proc. of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. Istanbul: ACM,
2021. 58–65. [doi: 10.1145/3458306.3460998]
[13] Shen M, Zhang JP, Xu K, Zhu LH, Liu JC, Du XJ. DeepQoE: Real-time measurement of video QoE from encrypted traffic with deep
learning. In: Proc. of the 28th IEEE/ACM Int’l Symp. on Quality of Service (IWQoS). Hangzhou: IEEE, 2020. 1–10. [doi: 10.1109/
IWQoS49365.2020.9212897]
[14] Gutterman C, Guo K, Arora S, Gilliland T, Wang XY, Wu L, Katz-Bassett E, Zussman G. Requet: Real-time QoE metric detection for
encrypted YouTube traffic. ACM Trans. on Multimedia Computing, Communications, and Applications, 2020, 16(2S): 71. [doi: 10.1145/
3394498]
[15] Wu H, Li X, Cheng G, Hu XY. Monitoring video resolution of adaptive encrypted video traffic based on HTTP/2 features. In: Proc. of the
IEEE INFOCOM 2021—IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS). Vancouver: IEEE, 2021. 1–6.
[doi: 10.1109/INFOCOMWKSHPS51825.2021.9484509]
[16] Wu H, Yu ZH, Cheng G, Guo SY. Identification of encrypted video streaming based on differential fingerprints. In: Proc. of the IEEE
INFOCOM 2020—IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS). Toronto: IEEE, 2020. 74–79. [doi: 10.
1109/INFOCOMWKSHPS50562.2020.9162914]
[17] Reed A, Kranch M. Identifying HTTPS-protected netflix videos in real-time. In: Proc. of the 7th ACM on Conf. on Data and Application
Security and Privacy. Scottsdale: ACM, 2017. 361–368. [doi: 10.1145/3029806.3029821]
[18] Shen M, Liu YT, Zhu LH, Xu K, Du XJ, Guizani N. Optimizing feature selection for efficient encrypted traffic classification: A
systematic approach. IEEE Network, 2020, 34(4): 20–27. [doi: 10.1109/MNET.011.1900366]
[19] Dubin R, Dvir A, Pele O, Hadar O. I know what you saw last minute—Encrypted HTTP adaptive video streaming title classification.
IEEE Trans. on Information Forensics and Security, 2017, 12(12): 3039–3049. [doi: 10.1109/TIFS.2017.2730819]
[20] Liu YT, Li S, Zhang CW, Sun Y, Liu QY. ITP-KNN: Encrypted video flow identification based on the intermittent traffic pattern of video
and K-nearest neighbors classification. In: Proc. of the 20th Int’l Conf. on Computational Science. Amsterdam: Springer, 2020. 279–293.
[doi: 10.1007/978-3-030-50417-5_21]
[21] Xu SC, Sen S, Mao ZM. CSI: Inferring mobile ABR video adaptation behavior under HTTPS and QUIC. In: Proc. of the 15th European
Conf. on Computer Systems. Heraklion: ACM, 2020. 33. [doi: 10.1145/3342195.3387558]
[22] Wu H, Yu ZH, Cheng G, Hu XY. Encrypted video recognition in large-scale fingerprint database. Ruan Jian Xue Bao/Journal of
Software, 2021, 32(10): 3310–3330 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6025.htm [doi: 10.13328/j.cnki.
jos.006025]
[23] Gu JX, Wang JL, Yu ZW, Shen KL. Traffic-based side-channel attack in video streaming. IEEE/ACM Trans. on Networking, 2019,
27(3): 972–985. [doi: 10.1109/TNET.2019.2906568]
[24] Afandi W, Bukhari SMAH, Khan MUS, Maqsood T, Khan ASU. Fingerprinting technique for YouTube videos identification in network
traffic. IEEE Access, 2022, 10: 76731–76741. [doi: 10.1109/ACCESS.2022.3192458]
[25] Bae S, Son M, Kim D, Park C, Lee J, Son S, Kim Y. Watching the watchers: Practical video identification attack in LTE networks. In:
Proc. of the 31st USENIX Security Symp. (USENIX Security 2022). Boston: USENIX Association, 2022. 1307–1324.
[26] Karagkioules T, Concolato C, Tsilimantos D, Valentin S. A comparative case study of HTTP adaptive streaming algorithms in mobile
networks. In: Proc. of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video. Taipei: ACM, 2017.
1–6. [doi: 10.1145/3083165.3083170]
[27] ISO/IEC 23009-1: 2014 Information technology—Dynamic adaptive streaming over HTTP (DASH)—Part 1: Media presentation
description and segment formats. 2024. https://www.iso.org/standard/65274.html
[28] Durak K, Akcay MN, Erinc YK, Pekel B, Begen AC. Evaluating the performance of Apple’s low-latency HLS. In: Proc. of the 22nd

