Anya: A Novel Video Identification Attack on Media Multiplexing

Although encryption is widely employed to protect video content during transmission, protocols like DASH can still inadvertently expose critical information about the online video being watched. Attackers can potentially identify the video a user is viewing by analysing undecrypted traffic patterns. Recently, however, popular video platforms like YouTube have updated their streaming technology by utilizing audio-video multiplexing to create dynamic traffic patterns, which significantly reduce the effectiveness of previous attack methods that treat audio and video traffic as separate tracks. In this paper, we are the first to reveal the vulnerabilities about this latest streaming technology and introduce a novel attack approach named Anya. By constraining audio and video timelines, Anya constructs stable audio-video fingerprints and enhances attack accuracy and efficiency through fuzzy searching strategy. Experimental results demonstrate that Anya achieves accuracy of 0.971, 0.933 in ideal and poor network scenarios, with only one minute of traffic eavesdropping time. Finally, we propose defense strategies for streaming platform developers to protect users' privacy.