  Website fingerprinting enables a local eavesdropper to determine which
websites a user is visiting over an encrypted connection. State-of-the-art
website fingerprinting attacks have been shown to be effective even against
Tor. Recently, lightweight website fingerprinting defenses for Tor have been
proposed that substantially degrade existing attacks: WTF-PAD and
Walkie-Talkie. In this work, we present Deep Fingerprinting (DF), a new website
fingerprinting attack against Tor that leverages a type of deep learning called
Convolutional Neural Networks (CNN) with a sophisticated architecture design,
and we evaluate this attack against WTF-PAD and Walkie-Talkie. The DF attack
attains over 98% accuracy on Tor traffic without defenses, better than all
prior attacks, and it is also the only attack that is effective against WTF-PAD
with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to
just 49.7% accuracy. In the more realistic open-world setting, our attack
remains effective, with 0.99 precision and 0.94 recall on undefended traffic.
Against traffic defended with WTF-PAD in this setting, the attack still can get
0.96 precision and 0.68 recall. These findings highlight the need for effective
defenses that protect against this new attack and that could be deployed in
Tor.
