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Network discovery using incomplete measurements.

机译：使用不完整的度量进行网络发现。

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摘要

Resolving characteristics of the Internet from empirical measurements is important in the development of new protocols, traffic engineering, advertising, and troubleshooting. Internet measurement campaigns commonly involve heavy network load probes that are usually non-adaptive and incomplete, and thus directly reveal only a fraction of the underlying network characteristics. This dissertation addresses the open problem of Internet characteristic discovery in an incomplete measurement regime. Using partially observed measurements, we specifically focus on the problems of Internet topology discovery, inferring the geographic location of Internet resources, and network anomaly detection.;The first problem addressed in this work is the inference of topological characteristics of the Internet from three distinct forms of incomplete measurements. Initial work demonstrates how Passive Measurements, potentially-incomplete passively observed characteristics of the network, can be used to infer topological structure, such as clustering and shared path lengths. The second form of missing measurements come in the form of a set of traceroute probes, where we obtain partial knowledge of route lengths between routers in the network. Using a novel statistical methodology, we show how unobserved links between routers can be detected. Finally, we develop a novel targeted delay-based tomographic methodology, which resolves the tree topology of a network with a methodology that only requires a number of directed measurements within a poly-logarithmic factor of derived lower bounds.;The second component of this dissertation focuses on two critical networking problems -- geographic location interference of Internet resources and network anomaly detection. In terms of geographic location inference, our methodology exploits a set of landmarks in the network with known geographic location and targeted latency probes to avoid erroneous measurements caused by non-line-of-sight routing of long network paths. The use of a novel embedding algorithm allows for the inferred geolocation of end hosts to be clustered in areas of large population density without explicitly defined population data. Finally, we examine detecting unforeseen anomalous events in a network. Using a limited training set of labeled anomalies, our new anomaly detection framework extracts signal characteristics of anomalous events and detects their occurrence across observed network-wide measurements.
机译：通过经验测量来解决Internet的特征对于开发新协议，流量工程，广告和故障排除很重要。 Internet测量活动通常涉及沉重的网络负载探测器，这些探测器通常是不自适应且不完整的，因此仅直接揭示了底层网络特征的一部分。本文在一个不完整的度量体系中解决了互联网特征发现的开放性问题。使用部分观察到的测量，我们特别关注Internet拓扑发现，推断Internet资源的地理位置以及网络异常检测的问题。本研究解决的第一个问题是从三种不同的形式推断Internet的拓扑特征不完整的测量。最初的工作演示了如何使用被动测量（可能不完整的被动观察到的网络特性）来推断拓扑结构，例如聚类和共享路径长度。丢失测量的第二种形式是通过一组traceroute探针的形式，在其中我们可以部分了解网络中路由器之间的路由长度。使用一种新颖的统计方法，我们展示了如何检测到路由器之间未观察到的链接。最后，我们开发了一种新颖的基于目标的基于延迟的层析成像方法，该方法使用一种仅需在导出的下界的对数因子内进行大量定向测量的方法即可解决网络的树形拓扑问题。重点关注两个关键的网络问题-Internet资源的地理位置干扰和网络异常检测。在地理位置推断方面，我们的方法利用网络中具有已知地理位置和目标延迟探测器的一组地标来避免由长距离网络路径的非视线路由引起的错误测量。新颖的嵌入算法的使用允许在没有明确定义的人口数据的情况下，将推断出的宿主主机的地理位置聚集在人口密度大的区域中。最后，我们检查检测网络中无法预料的异常事件。通过使用有限的标记异常训练集，我们的新异常检测框架可以提取异常事件的信号特征，并在观察到的整个网络范围内的测量中检测它们的发生。

著录项

  • 作者

    Eriksson, Brian.;

  • 作者单位

    The University of Wisconsin - Madison.;

  • 授予单位 The University of Wisconsin - Madison.;
  • 学科 Engineering Electronics and Electrical.;Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 201 p.
  • 总页数 201
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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