Peering Through the Veil: A Segment-Based Approach for VPN Encapsulated Video Title Identification

With the widespread adoption of the Internet and the continuous evolution of digital media technologies, video stream has increasingly become a dominant part of overall Internet traffic. The spread of harmful videos in social networks makes it important to collect evidence and regulate them. With the growth of Internet video stream, identifying video titles in VPN encapsulated environments has become increasingly challenging, as Virtual Private Networks (VPNs) encapsulate already encrypted video stream. To address this challenge, we extract stable video segments features from VPN encapsulated traffic based on DASH and HLS protocols, and then use these features to train hybrid CNN-LSTM models for accurate video title identification. Furthermore, the method filters background traffic and leverages unidirectional flow characteristics to handle asymmetric routing in real networks. Experimental results show that this approach achieves high accuracy, surpassing existing methods and demonstrates robustness when handling VPN encapsulated video streams, even under interference from background traffic and complex routing environments.