Network traffic speeds and volumes present practical challenges to analysis. Packet thinning and flow aggregation protocols provide smaller structured data summaries, but conversely impede statistical inference. Methods which model traffic propagation typically do not account for the packet thinning and aggregation in their analysis and are of limited practical use. We introduce a likelihood-based analysis which fully incorporates packet thinning and flow aggregation. Inferences can hence be made for models on the level of individual packets while only observing thinned flow summaries. We establish consistency of the resulting maximum likelihood estimator, derive bounds on the volume of traffic which should be observed to achieve a desired degree of efficiency, and identify an ideal family of models. The robust performance of the estimator is examined through simulated analyses and an application on a publicly accessible trace which captured in excess of 36 m packets over a 1 minute period.

中文翻译：

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从细化流摘要建模数据包传输的基于似然的推理

网络流量速度和流量对分析提出了实际挑战。数据包细化和流聚合协议提供较小的结构化数据摘要，但反过来阻碍了统计推断。对流量传播进行建模的方法通常不会在分析中考虑数据包细化和聚合，并且实际用途有限。我们引入了一种基于可能性的分析，它完全结合了数据包细化和流聚合。因此，可以对单个数据包级别的模型进行推断，同时仅观察细化的流摘要。我们建立了由此产生的最大似然估计量的一致性，得出了应该观察到的交通量界限，以实现所需的效率程度，并确定一个理想的模型族。