Encryption on the internet with the shift to HTTPS has been an important step to improve the privacy of internet users. However, there is an increasing body of work about extracting information from encrypted internet traffic without having to decrypt it. Such attacks bypass security guarantees assumed to be given by HTTPS and thus need to be understood. Prior works showed that the variable bitrates of video streams are sufficient to identify which video someone is watching. These works generally have to make trade-offs in aspects such as accuracy, scalability, robustness, etc. These trade-offs complicate the practical use of these attacks. To that end, we propose a deep metric learning framework based on the triplet loss method. Through this framework, we achieve robust, generalisable, scalable and transferable encrypted video stream detection. First, the triplet loss is better able to deal with video streams not seen during training. Second, our approach can accurately classify videos not seen during training. Third, we show that our method scales well to a dataset of over 1000 videos. Finally, we show that a model trained on video streams over Chrome can also classify streams over Firefox. Our results suggest that this side-channel attack is more broadly applicable than originally thought. We provide our code alongside a diverse and up-to-date dataset for future research.

翻译：互联网加密随着HTTPS的普及成为提升用户隐私的重要一步。然而，目前有越来越多的研究致力于从加密互联网流量中提取信息，而无需对其进行解密。此类攻击绕过了HTTPS所确保的安全保障，因此亟需理解。先前研究表明，视频流的可变比特率足以识别某人正在观看的视频。这些研究通常需要在准确性、可扩展性、鲁棒性等方面做出权衡，从而限制了这些攻击在实际应用中的可行性。为此，我们提出一种基于三元组损失方法的深度度量学习框架。通过该框架，我们实现了对加密视频流的鲁棒、可泛化、可扩展且可迁移的检测。首先，三元组损失能更好地处理训练中未见过的视频流。其次，我们的方法可准确分类训练中未出现过的视频。第三，我们证明该方法能够很好地扩展至包含超过1000个视频的数据集。最后，我们表明，基于Chrome视频流训练的模型也能对Firefox上的视频流进行分类。研究结果表明，这种侧信道攻击的适用性比最初认为的更为广泛。我们提供代码及一个多样化且最新的数据集，以供未来研究使用。