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互联网协议语音流量的负载特征描述与异常检测。

Load characterization and anomaly detection for voice over IP traffic.

作者信息

Mandjes Michel, Saniee Iraj, Stolyar Alexander L

机构信息

Center for Mathematics and Computer Science (CWI), 1090 GB Amsterdam, The Netherlands.

出版信息

IEEE Trans Neural Netw. 2005 Sep;16(5):1019-26. doi: 10.1109/TNN.2005.853427.

Abstract

We consider the problem of traffic anomaly detection in IP networks. Traffic anomalies typically arise when there is focused overload or when a network element fails and it is desired to infer these purely from the measured traffic. We derive new general formulae for the variance of the cumulative traffic over a fixed time interval and show how the derived analytical expression simplifies for the case of voice over IP traffic, the focus of this paper. To detect load anomalies, we show it is sufficient to consider cumulative traffic over relatively long intervals such as 5 min. We also propose simple anomaly detection tests including detection of over/underload. This approach substantially extends the current practice in IP network management where only the first-order statistics and fixed thresholds are used to identify abnormal behavior. We conclude with the application of the scheme to field data from an operational network.

摘要

我们考虑IP网络中的流量异常检测问题。流量异常通常在出现集中过载或网络元件发生故障时出现,并且希望仅从测量流量中推断出这些情况。我们推导出了固定时间间隔内累积流量方差的新通用公式,并展示了对于本文重点关注的IP语音流量情况,所推导的解析表达式是如何简化的。为了检测负载异常,我们表明考虑相对较长时间间隔(如5分钟)内的累积流量就足够了。我们还提出了简单的异常检测测试,包括过载/欠载检测。这种方法极大地扩展了IP网络管理中的当前做法,目前仅使用一阶统计量和固定阈值来识别异常行为。我们最后将该方案应用于来自运营网络的现场数据。

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