Title: p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning,
Abstract: Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to website fingerprinting. We show that unsupervised DNNs can be
used to extract low-dimensional feature vectors that improve the performance of
state-of-the-art website fingerprinting attacks. When used as classifiers, we
show that they can match or 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 show that DNNs
can be used to predict the fingerprintability of a website based on its
contents, achieving 99% accuracy on a data set of 4500 website downloads.