Blind Adversarial Network Perturbations

Milad Nasr; Alireza Bahramali; Amir Houmansadr

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arXiv · 2002.06495

Blind Adversarial Network Perturbations

Milad Nasr·Alireza Bahramali·Amir Houmansadr

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Abstract

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.

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Milad Nasr, Alireza Bahramali, Amir Houmansadr. 2020-02-16. Blind Adversarial Network Perturbations. https://arxiv.org/abs/2002.06495

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