Efficient Short Video Identification Attack for Scenarios with Hybrid Transmission Modes and Preloading Mechanism

To protect user privacy, video traffic is usually encrypted during transmission. Some research has been con-ducted to implement video identification attacks by analyzing the features of video traffic. However, short video platforms use hybrid transmission modes and a preloading mechanism to improve user experience. These new characteristics make video identification attacks targeted at long videos not appliable to short videos. In this paper, we proposed a method for extracting and correcting the hybrid fingerprints of application layer video resources, and designed an SVP-DOHMM matching method for video identification. Our method can identify each video for scenarios with hybrid transmission modes and preloading mechanism. We implemented our method on the dataset con-taining more than 100,000 video fingerprints of a short video platform. The experimental results show that the accuracy can reach 98.16% by sniffing the traffic for only 10 seconds in the closed world and can identify videos with 97.64 % accuracy in the open world.