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Real-time Encrypted Traffic Identification using Machine Learning

机译：使用机器学习进行实时加密流量识别

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摘要

Accurate network traffic identification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. The emergence of many new encrypted applications which use dynamic port numbers and masquerading techniques causes the most challenging problem in network traffic identification field. One of the challenging issues for existing traffic identification methods is that they can’t classify online encrypted traffic. To overcome the drawback of the previous identification scheme and to meet the requirements of the encrypted network activities, our work mainly focuses on how to build an online Internet traffic identification based on flow information. We propose real-time encrypted traffic identification based on flow statistical characteristics using machine learning in this paper. We evaluate the effectiveness of our proposed method through the experiments on different real traffic traces. By experiment results and analysis, this method can classify online encrypted network traffic with high accuracy and robustness.
机译：准确的网络流量识别在流量工程，QoS和入侵检测等许多领域都起着重要作用。使用动态端口号和伪装技术的许多新加密应用程序的出现引起了网络流量识别领域最具挑战性的问题。现有流量识别方法面临的挑战之一是它们无法对在线加密流量进行分类。为了克服现有识别方案的弊端，并满足加密网络活动的要求，我们的工作主要集中在如何基于流信息建立在线互联网流量识别上。本文提出了一种基于流量统计特征的实时加密流量识别算法，该算法利用机器学习技术。我们通过在不同的真实交通轨迹上进行实验来评估我们提出的方法的有效性。通过实验结果和分析，该方法可以对在线加密网络流量进行分类，具有较高的准确性和鲁棒性。

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