Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations

Deep neural networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e.g., statistical) techniques by large margins. However, deep neural networks are known to be vulnerable to adversarial examples: adversarial inputs to the model that get labeled incorrectly by the model due to small adversarial perturbations. In this paper, for the first time, we show that an adversary can defeat DNN-based traffic analysis techniques by applying adversarial perturbations on the patterns of live network traffic.

Applying adversarial perturbations (examples) on traffic analysis classifiers faces two major challenges. First, the perturbing party (i.e., the adversary) should be able to apply the adversarial network perturbations on live traffic, with no need to buffering traffic or having some prior knowledge about upcoming network packets. We design a systematic approach to create adversarial perturbations that are independent of their target network connections, and therefore can be applied in real-time on live traffic. We therefore call such adversarial perturbations blind.

Second, unlike image classification applications, perturbing traffic features is not straight-forward as this needs to be done while preserving the correctness of dependent traffic features. We address this challenge by introducing remapping functions that we use to enforce different network constraints while creating blind adversarial perturbations.

Our blind adversarial perturbations algorithm is generic and can be applied on various types of traffic classifiers. We demonstrate this by implementing a Tor pluggable transport that applies adversarial perturbations on live Tor connections to defeat DNN-based website fingerprinting and flow correlation techniques, the two most-studied types of traffic analysis. We show that our blind adversarial perturbations are even transferable between different models and architectures, so they can be applied by blackbox adversaries. Finally, we show that existing countermeasures perform poorly against blind adversarial perturbations, therefore, we introduce a tailored countermeasure.