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Desktops and laptops can be maliciously exploited to violate privacy. Thereare two main types of attack scenarios: active and passive. In this paper, weconsider the passive scenario where the adversary does not interact activelywith the device, but he is able to eavesdrop on the network traffic of thedevice from the network side. Most of the Internet traffic is encrypted andthus passive attacks are challenging. Previous research has shown thatinformation can be extracted from encrypted multimedia streams. This includesvideo title classification of non HTTP adaptive streams (non-HAS). This paperpresents an algorithm for encrypted HTTP adaptive video streaming titleclassification. We show that an external attacker can identify the video titlefrom video HTTP adaptive streams (HAS) sites such as YouTube. To the best ofour knowledge, this is the first work that shows this. We provide a large dataset of 10000 YouTube video streams of 100 popular video titles (each titledownloaded 100 times) as examples for this task. The dataset was collectedunder real-world network conditions. We present several machine algorithms forthe task and run a through set of experiments, which shows that ourclassification accuracy is more than 95%. We also show that our algorithms areable to classify video titles that are not in the training set as unknown andsome of the algorithms are also able to eliminate false prediction of videotitles and instead report unknown. Finally, we evaluate our algorithmsrobustness to delays and packet losses at test time and show that a solutionthat uses SVM is the most robust against these changes given enough trainingdata. We provide the dataset and the crawler for future research.

I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification