Examining Adversarial Examples as Defensive Approach Against Web Fingerprinting Attacks

Abstract

In the age of online surveillance, and the growth in privacy and security concerns for individuals activities over the internet. Tor browser is a widely used anonymisation network offering security and privacy-enhanced features to protect users online. However, web fingerprinting attacks (WF) have been a challenging threat that aims to deanonymise users browsing activities over Tor. This interdisciplinary project contributes to defending against WF attacks by employing the “attack-on-attack” approach, where Adversarial Examples (AEs) attacks are launched to exploit existing vulnerabilities in the neural network architecture. The FGSM and DeepFool construction methods are implemented to introduce perturbed data to these models and lead them to misclassify, significantly decreasing the classifier prediction accuracy.