I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification
Ran Dubin, Amit Dvir, Ofir Pele, Ofer Hadar

TL;DR
This paper demonstrates that it is possible to classify encrypted HTTP adaptive video streams, such as YouTube videos, with over 95% accuracy using machine learning, revealing privacy vulnerabilities in encrypted streaming traffic.
Contribution
The authors introduce the first algorithm capable of classifying encrypted HTTP adaptive video streams and provide a large dataset for future research.
Findings
Achieved over 95% classification accuracy.
Algorithms can identify unknown video titles.
SVM-based approach is most robust against delays and packet loss.
Abstract
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…
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Taxonomy
MethodsSupport Vector Machine
