p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

Authors: Se Eun Oh (University of Minnesota), Saikrishna Sunkam (University of Minnesota), Nicholas Hopper (University of Minnesota)

Volume: 2019
Issue: 3
Pages: 191–209
DOI: https://doi.org/10.2478/popets-2019-0043

Download PDF

Abstract: Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution-NonCommercial-NoDerivs license.