Pioneer: Encrypted Video Traffic Identification for Mixed Transmission of Video-Audio Segments

Weitao Tang, Taizhong Xu, Meijie Du, Die Hu, Xu Tang, Qingyun Liu

Abstract: The spread of harmful content via video has made video traffic identification crucial for network regulation. In the new transmission mode, audio and video segments are mixed to combine into video chunks. However, in poor networks, such combination is unstable, and video chunks may be lost and retransmitted. To address these challenges, this paper proposes Pioneer, an encrypted video traffic identification method for mixed transmission of audio and video segments. We introduce a precise video chunk reconstruction method for video traffic encrypted by both TLS and QUIC. Additionally, we propose Pseudo-Siamese Attention-Convolutional Network (PSACN) to calculate the similarity between traffic and video, leveraging contrastive learning during training to mitigate the impact of poor networks. Pioneer significantly improves accuracy compared with state-of-the-art (SOTA) methods under various network environments. Notably, this is the first study to address this emerging new transmission mode.

External IDs:dblp:conf/icmcs/TangXDHTL25