  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 \emph{adversarial perturbations} on the patterns of
\emph{live} network traffic.
