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首页> 外文期刊>IEEE transactions on dependable and secure computing >Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark (Web)
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Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark (Web)

机译：匿名服务Tor，I2P，Jondony：在黑暗中进行分类（Web）

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摘要

Traffic Classification (TC) is an important tool for several tasks, applied in different fields (security, management, traffic engineering, R&D). This process is impaired or prevented by privacy-preserving protocols and tools, that encrypt the communication content, and (in case of anonymity tools) additionally hide the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide classification results with the aim of investigating to which degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of other considered anonymity tools, using five machine learning classifiers. Initially, flow-based TC is considered, and the effects of feature importance and temporal-related features to the network are investigated. Additionally, the role of finer-grained features, such as the (joint) histogram of packet lengths (and inter-arrival times), is determined. Successively, "early" TC of anonymous networks is analyzed. Results show that the considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.87% and 99.80%, in case of flow-based and early-TC, respectively), telling even the specific application generating the traffic (with an accuracy of 73.99% and 66.76%, in case of flow-based and early-TC, respectively).
机译：流量分类（TC）是应用于不同领域的多个任务的重要工具（安全，管理，流量工程，R＆D）。通过隐私保留协议和工具损害或防止该过程，该协议和工具加密通信内容，（在匿名工具的情况下）另外隐藏通信的源，目的地和性质。在本文中，利用2017年发布的公共数据集，我们提供分类结果，目的是调查可以识别特定匿名工具（以及它隐藏的流量）的程度，当与其他被视为匿名工具的流量相比，使用五种机器学习分类器。最初，考虑了基于流的Tc，并研究了特征重要性和与网络的时间相关特征的影响。另外，确定更精细的特征的作用，例如（关节）分组长度（和到达互移时间）的直方图。连续地，分析了“早期”TC的匿名网络。结果表明，在基于流动和早期的情况下，可以轻松地区分（准确度为99.87％和99.80％的准确性）所考虑的匿名网络（TOR，I2P，Jondym），即使是生成的特定应用程序也会讲述在基于流量和早期Tc的情况下，交通（精度为73.99％和66.76％）。

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