Robust Machine Learning for Encrypted Traffic Classification

Desktops and laptops can be maliciously exploited to violate privacy. In this paper, we consider the daily battle between the passive attacker who is targeting a specific user against a user that may be adversarial opponent. In this scenario, while the attacker tries to choose the best vector attack by surreptitiously monitoring the victim’s encrypted network traffic in order to identify user’s parameters such as the Operating System (OS), browser and apps. The user may use tools such as a Virtual Private Network (VPN) or even change protocols parameters to protect his/her privacy. We provide a large dataset of more than 20,000 examples for this task. We run a comprehensive set of experiments, that achieves high (above 85%) classification accuracy, robustness and resilience to changes of features as a function of different network conditions at test time. We also show the effect of a small training set on the accuracy.

桌面电脑与笔记本电脑可能遭受恶意利用，以侵犯个人隐私。在本研究中，我们探讨了针对特定用户的被动攻击者与可能构成对抗性对手的用户之间的日常较量。在此场景中，攻击者试图通过暗中监控受害者的加密网络流量，以识别用户的参数，如操作系统（OS）、浏览器和应用程序。用户可能会使用虚拟专用网络（VPN）等工具，甚至更改协议参数以保护其隐私。为此，我们提供了一组包含超过20,000个实例的大型数据集。我们进行了一系列全面实验，实现了高（超过85%）的分类准确率、鲁棒性以及对测试时不同网络条件下特征变化的抗变性。此外，我们还展示了小规模训练集对准确率的影响。