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

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 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.