@inproceedings{
tang2024zenith,
title={Zenith: Real-time Identification of {DASH} Encrypted Video Traffic with Distortion},
author={Weitao Tang and Jianqiang Li and Meijie Du and Die Hu and Qingyun Liu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=aE2ZGYxrAe}
}
Some video traffic carries harmful content, such as hate speech and child abuse, primarily encrypted and transmitted through Dynamic Adaptive Streaming over HTTP (DASH). Promptly identifying and intercepting traffic of harmful videos is crucial in network regulation. However, QUIC is becoming another DASH transport protocol in addition to TCP. On the other hand, complex network environments and diverse playback modes lead to significant distortions in traffic. The issues above have not been effectively addressed. This paper proposes a real-time identification method for DASH encrypted video traffic with distortion, named Zenith. We extract stable video segment sequences under various itags as video fingerprints to tackle resolution changes and propose a method of traffic fingerprint extraction under QUIC and VPN. Subsequently, simulating the sequence matching problem as a natural language problem, we propose Traffic Language Model (TLM), which can effectively address video data loss and retransmission. Finally, we propose a frequency dictionary to accelerate Zenith's speed further. Zenith significantly improves accuracy and speed compared to other SOTA methods in various complex scenarios, especially in QUIC, VPN, automatic resolution, and low bandwidth. Zenith requires traffic for just half a minute of video content to achieve precise identification, demonstrating its real-time effectiveness. The project page is available at https://anonymous.4open.science/r/Zenith-Anonymous.
Zenith: Real-time Identification of DASH Encrypted Video Traffic with Distortion
Weitao Tang Jianqiang Li Meijie Du Die Hu Qingyun Liu