Defending Against Deep Learning-Based Traffic Fingerprinting Attacks With Adversarial Examples

In an increasingly digital and interconnected world, online anonymity and privacy are paramount issues for Internet users. To address this, tools like The Onion Router (Tor) offer anonymous and private communication by routing traffic through multiple relays with multiple layers of encryption. However, traffic fingerprinting attacks have threatened anonymity and privacy. In response, the community has proposed additional defenses for Tor, but fingerprinting techniques that utilize deep neural networkss (DNNs) have undermined many of these defenses. The latest defenses that are both lightweight and robust against DNNs use adversarial examples, but these defenses require either the full traffic trace beforehand or a database of pre-computed adversarial examples. We propose Prism , a defense against fingerprinting attacks that utilizes adversarial examples with neither prior access to the full traffic trace nor a database. We describe a novel method of adversarial example generation as input is learned over time. Prism injects these adversarial examples into the Tor traffic stream to prevent DNNs from accurately classifying both websites and videos that a user is viewing, even if the DNN is hardened by adversarial training. We also show that the Tor network could implement Prism entirely on relays under certain conditions, extending the defense to users who may run Tor on devices without graphics processing units.