Backdoor Poisoning of Encrypted Traffic Classifiers

J. Holodnak, Olivia Brown, J. Matterer, Andrew Lemke
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引用次数: 2

Abstract

Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks against an encrypted network traffic classifier. We consider attacks based on padding network packets, which has the benefit of preserving the functionality of the network traffic. In particular, we consider a handcrafted attack, as well as an optimized attack leveraging universal adversarial perturbations. We find that poisoning attacks can be extremely successful if the adversary has the ability to modify both the labels and the data (dirty label attacks) and somewhat successful, depending on the attack strength and the target class, if the adversary perturbs only the data (clean label attacks).
加密流量分类器的后门中毒
近年来的重要研究集中在将深度神经网络模型应用于网络流量分类问题上。与此同时,关于深度神经网络在训练和推理过程中对对抗性输入的脆弱性的文章也很多。在这项工作中,我们考虑对加密的网络流量分类器发起后门投毒攻击。我们考虑基于填充网络数据包的攻击,这有利于保留网络流量的功能。特别是,我们考虑了手工攻击,以及利用普遍对抗性扰动的优化攻击。我们发现,如果攻击者有能力修改标签和数据(脏标签攻击),那么中毒攻击就会非常成功;如果攻击者只干扰数据(干净标签攻击),那么中毒攻击就会取得一定程度的成功,这取决于攻击强度和目标类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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