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Measuring the accuracy of open-source payload-based traffic classifiers using popular Internet applications

机译:使用流行的Internet应用程序测量基于开源负载的流量分类器的准确性

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Open-source payload-based traffic classifiers are frequently used as a source of ground truth in the traffic classification research field. However, there have been no comprehensive studies that provide evidence that the classifications produced by these software tools are sufficiently accurate for this purpose. In this paper, we present the results of an investigation into the accuracy of four open-source traffic classifiers (L7 Filter, nDPI, libprotoident and tstat) using packet traces captured while using a known selection of common Internet applications, including streaming video, Steam and World of Warcraft. Our results show that nDPI and libprotoident provide the highest accuracy among the evaluated traffic classifiers, whereas L7 Filter is unreliable and should not be used as a source of ground truth.
机译:基于开源有效负载的流量分类器在流量分类研究领域中经常被用作地面真理的来源。但是,还没有全面的研究提供证据证明这些软件工具产生的分类对于此目的足够准确。在本文中,我们使用四个已知的常见Internet应用程序(包括流视频,Steam)来捕获数据包跟踪,从而对四个开源流量分类器(L7过滤器,nDPI,libprotoident和tstat)的准确性进行了调查。和魔兽世界。我们的结果表明,nDPI和libprotoident在所评估的流量分类器中提供了最高的准确性,而L7过滤器是不可靠的,因此不应被用作地面真实性的来源。

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