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Identification of User Application by an External Eavesdropper using Machine Learning Analysis on Network Traffic

机译：使用外部学习者对网络流量的机器学习分析来识别用户应用程序

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摘要

An eavesdropper may infer the computer applications a person uses by collecting and analyzing the network traffic they generate. Such inference may be performed despite applying encryption on the generated packets. In this paper, we investigate the extent of the ability of several machine learning algorithms to perform this privacy breach on the network traffic generated by a user. We measure their accuracy in identifying different applications by analyzing several statistical properties of the generated traffic rather than looking into the encrypted content. We compare the performance of these algorithms and select the one with higher precision; random forest. We also evaluate the application of packet padding to modify the packet length to avoid identification by machine learning algorithms. We test the effect of packet padding on the identification ability of the various machine-learning algorithms. We investigate the performance of the random forest algorithm in detail when applied to intact and padded traffic. We show that padding may decrease the efficacy of a machine-learning algorithm when used for application classification.
机译：窃听者可以通过收集和分析他们生成的网络流量来推断一个人使用的计算机应用程序。尽管对所生成的分组应用了加密，也可以执行这种推断。在本文中，我们研究了几种机器学习算法对用户生成的网络流量执行此隐私破坏的能力的程度。我们通过分析生成的流量的几种统计属性（而不是查看加密的内容）来衡量它们在识别不同应用程序方面的准确性。我们比较了这些算法的性能，并选择了精度更高的算法;随机森林。我们还评估了数据包填充的应用，以修改数据包长度，从而避免通过机器学习算法进行识别。我们测试了数据包填充对各种机器学习算法的识别能力的影响。当我们将其应用于完整流量和填充流量时，我们将详细研究随机森林算法的性能。我们表明，填充用于应用分类时可能会降低机器学习算法的功效。

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