Towards Video Fingerprinting Attacks over Tor
Abstract
As web users resort to adopting encrypted networks like Tor to protect their anonymity online, adversaries find new ways to collect their private information. Since videos over the internet are a major source of recruitment, training, incitement to commit acts of terrorism, and more, this project envisions developing a machine learning algorithm that can help the Department of Defense find terrorists who take advantage of the dark web to help promote extremist ideology. This thesis describes the steps for training a machine learning classifier in a closed-world scenario to predict YouTube video patterns over an encrypted network like Tor. Our results suggest an adversary may predict the video that a user downloads over Tor with up to 92% accuracy, or may predict the length of a video with error as low as 5.3s. Similar to known website fingerprinting attacks, we show that Tor is susceptible to video fingerprinting, suggesting that Tor does not provide the level of anonymity as previously thought.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2021
- Accession Number
- AD1164219
Entities
People
- Carlos D. Campuzano
Organizations
- Naval Postgraduate School