The time-sync comments have been prevalent in modern live streaming systems to provide a real-time interaction experience for viewers. Whereas, the time-sync comments traffic can also act as a delicate fingerprint of encrypted live channels, leading to potential risks of privacy leakage. Most of previous video channel identification strategies with video bitrate-based fingerprint presume strict requirements on the implementation environments, which often assume that there is no interference from irrelevant traffic flows or network conditions. However, the time-sync comments sessions are distinct from other irrelevant traffic flows, and the traffic pattern is resilient to various network conditions, e.g., bandwidth limitation and transmission delay. In this paper, we design a system for encrypted live channel identification with timesync comments traffic analysis. Specifically, both the inter-application and inner-application traffic filters are proposed to eliminate the irrelevant traffic flows, respectively. Further, a comment rate estimation method is developed through investigation of relationship between comment number, comment length and packet length. Finally, the dynamic time warping algorithm is improved for similarity matching in delay tolerant environment. In order to evaluate the system performance, we setup the prototype system with AWS EC2 server and utilize the real world trace data from Youtube and BiliBili. The experimental results show that the accuracy of the filter can reach 93.2%, and the accuracy of the comment rate estimation method can reach up to 91%. The match accuracy between fingerprint and comment rate can reach 92.1% within 200 seconds eavesdropping, which is 2% higher than using bitrate fingerprint and traffic pattern in the latest research, and can be increased to 98.2% when the eavesdropping time extends to 500 seconds.
As the video streaming traffic grows exponentially nowadays, variable bitrate (VBR) encoding has been widely utilized by modern live video streaming service providers, such as YouTube, TikTok, and Twitch. However, video bitrate can be a delicate fingerprint of the video streaming, leading to risks of privacy leakage. There are several studies that attempt to eavesdrop the privacy from encrypted video streaming, but most of them presume strict requirements on the implementation environments and have great limitations when noise interference exists. Actually, the video traffic from the multimedia edge server is distinct from interapplication traffic flows due to device customization and can be identified even if there are noise interferences or the victim in a weak network condition. In this paper, a video traffic identification method is proposed to identify the encrypted video streaming from multimedia edge server under the interference of irrelevant traffic flows. Initially, we use an interapplication filter to identify the traffic from the edge server. Then, a longest-common-subsequence (LCS)-based method is developed for similarity matching to resist the noise interference from unpredictable burst traffic and network environment variations. In order to evaluate the system performance, we setup the prototype system with an AWS EC2 server and a raspberry pi device, then utilize the real-world trace data for pushing movies to victims. The experimental results show that the accuracy of our proposed strategy can reach 89.1% within 140 seconds eavesdropping even mixed with 14% noise interference.
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