TOWARDS VIDEO FINGERPRINTING ATTACKS OVER TOR
Loading...
Authors
Campuzano, Carlos D.
Subjects
Tor
website fingerprinting
video fingerprinting
anonymity
machine learning classifier
deep learning
dark web
website fingerprinting
video fingerprinting
anonymity
machine learning classifier
deep learning
dark web
Advisors
Barton, Armon C.
Date of Issue
2021-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
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.
Type
Thesis
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.