Abstract
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 time-sync 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(DTW) 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.
Highlights
Live streaming service with time-sync comment has been an emerging killer application in recent years, which quickly sweeps across the world, in terms of online streaming for various social activities, such as live e-commerce, sports events, or festival ceremony
TRAFFIC FLOWS OF TIME-SYNC COMMENTS AS FINGERPRINTS Video traffic analysis is usually performed with variable bitrate encoding (VBR), in which the bitrate can be adaptive to the video content and network condition fluctuations
A real dataset was captured from three YouTube live channel and a prototype system was presented for performance evaluation
Summary
Live streaming service with time-sync comment has been an emerging killer application in recent years, which quickly sweeps across the world, in terms of online streaming for various social activities, such as live e-commerce, sports events, or festival ceremony. We will present a channel identification method for encrypted live streaming with time-sync comments. A convolutional neural network is trained for feature extraction and filter the relevant packets of time-sync comments in the encrypted traffic flow from dedicated server. DATA ANALYSIS AND MOTIVATION we will present the real world data analysis to illustrate the traffic flow features of video bitrate and timesync comments, as well as the influence of traffic noises. A. TRAFFIC FLOWS OF TIME-SYNC COMMENTS AS FINGERPRINTS Video traffic analysis is usually performed with variable bitrate encoding (VBR), in which the bitrate can be adaptive to the video content and network condition fluctuations. Further preprocess still needs to be performed to extract the comment traffic as a fine-gained fingerprint
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