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Stability of flow features for the identification of Internet applications

机译：用于识别Internet应用程序的流功能的稳定性

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摘要

One important requirement associated with the deployment of large scale classification infrastructures is the portability of classifiers, which allows a small number of pre-trained classifiers to be used on many sites and time periods. The portability can be severely degraded if the flow features used in the classification process lack stability, i.e. if they do not preserve their most relevant statistical properties across different sites and time periods. In this paper we propose a statistical procedure to evaluate the stability of flow features, which resorts to the notion of effect size. The procedure is used challenge the stability of popular flow features, such as the direction and size of the first four packets of a TCP connection. Our results, obtained with three high-quality traffic traces, clearly show that only some applications are portable, when using these features as discriminators. We also provide evidence of these findings based on the operation of the protocols underlying the Internet applications.
机译：与大规模分类基础架构的部署相关的一项重要要求是分类器的可移植性，它允许在许多站点和时间段上使用少量经过预先训练的分类器。如果在分类过程中使用的流量特征缺乏稳定性，即如果它们在不同站点和时间段内未保留其最相关的统计属性，则可移植性可能会严重降低。在本文中，我们提出了一种统计程序来评估流动特征的稳定性，这要根据效应大小的概念来进行。使用该过程将挑战常用流功能的稳定性，例如TCP连接的前四个数据包的方向和大小。我们的结果是从三条高质量的流量跟踪中获得的，清楚地表明，当使用这些功能作为区分器时，只有某些应用程序是可移植的。我们还基于Internet应用程序底层协议的操作来提供这些发现的证据。

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