Realistic Website Fingerprinting By Augmenting Network Trace
Alireza Bahramali Ardavan Bozorgi Amir Houmansadr
Website Fingerprinting (WF) is considered a major threat to the anonymity of
Tor users (and other anonymity systems). While state-of-the-art WF techniques
have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks
(DNN), several recent works have questioned the practicality of such WF attacks
in the real world due to the assumptions made in the design and evaluation of
these attacks. In this work, we argue that such impracticality issues are
mainly due to the attacker's inability in collecting training data in
comprehensive network conditions, e.g., a WF classifier may be trained only on
samples collected on specific high-bandwidth network links but deployed on
connections with different network conditions. We show that augmenting network
traces can enhance the performance of WF classifiers in unobserved network
conditions. Specifically, we introduce NetAugment, an augmentation technique
tailored to the specifications of Tor traces. We instantiate NetAugment through
semi-supervised and self-supervised learning techniques. Our extensive
open-world and close-world experiments demonstrate that under practical
evaluation settings, our WF attacks provide superior performances compared to
the state-of-the-art; this is due to their use of augmented network traces for
training, which allows them to learn the features of target traffic in
unobserved settings. For instance, with a 5-shot learning in a closed-world
scenario, our self-supervised WF attack (named NetCLR) reaches up to 80%
accuracy when the traces for evaluation are collected in a setting unobserved
by the WF adversary. This is compared to an accuracy of 64.4% achieved by the
state-of-the-art Triplet Fingerprinting [35]. We believe that the promising
results of our work can encourage the use of network trace augmentation in
other types of network traffic analysis.