Encrypted Live Streaming Channel Identification With Time-Sync Comments

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 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.

Noise-Resistant Video Channel Identification

“…Nevertheless, most sequence matching methods do not consider the matching effectiveness in interference environment. anks to the powerful representation ability of deep learning, similarity learning can accommodate heterogeneous features in the sophisticated environments, and there are several deep-learning-based methods like the CNN-based solution [30,31], and the LSTM-based solution [32]. However, deep-learning-based models usually need online training to adapt the latest features, and the computational cost is very high.…”