从抽样流量统计推断原始流量模式

Tatsuya Mori, R. Kawahara, N. Kamiyama, Shigeaki Harada
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引用次数: 4

摘要

包采样已经成为一种实用的、不可缺少的流量统计测量手段。最近的研究表明,分析流量模式对于检测网络异常至关重要。我们可能无法从采样的流量统计数据中正确地推断出原始的交通模式,因为采样过程会消除大量关于小流量的信息,这些信息在确定交通模式特征方面起着至关重要的作用。在本文中,我们首先展示了采样过程如何使用测量数据消除原始统计量的一个例子。然后,我们展示了经验例子,表明即使我们使用不完整数据的统计推断方法(即EM算法)进行抽样流量统计,也不能正确推断原始流量模式。最后,我们证明了关于原始流量统计的附加信息,即未采样流量的数量,有助于使用采样流量统计跟踪原始流量模式的变化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring Original Traffic Pattern from Sampled Flow Statistics
Packet sampling has become a practical and indispensable means to measure flow statistics. Recent studies have demonstrated that analyzing traffic patterns is crucial in detecting network anomalies. We may not be able to infer the original traffic patterns correctly from the sampled flow statistics because sampling process wipes out a lot of information about small flows, which play a vital role in determining the characteristics of traffic patterns. In this paper, we first show an example of how the sampling process wipes out the original statistics using measured data. Then, we show empirical examples indicating that the original traffic pattern cannot be inferred correctly even if we use a statistical inference method for incomplete data, i.e., the EM algorithm, for sampled flow statistics. Finally, we show that additional information about the original flow statistics, the number of unsampled flows, is helpful in tracking the change in original traffic patterns using sampled flow statistics
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