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