CIF: Small: Collaborative Research: Network Event Detection with Multistream Observations

CIF：小型：协作研究：通过多流观察进行网络事件检测

• 批准号: 1617789
• 负责人: Yingbin Liang
• 金额: $ 22万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617789&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Network

The goal in network event detection is to detect the existence of a set of nodes over a large network whose observations reflect the occurrence of an unusual event. Existing studies of network event detection have been mainly from two perspectives. The first is data-driven without assuming any underlying statistical model, and is typically applicable to more general data sets, but may not come with performance guarantees. The second perspective is model-driven, with certain statistical distributions (e.g., Gaussian) assumed for the data, and usually comes with performance guarantees, but may be limited to applications where the data fit the model. The goal in this project is to explore a framework for network event detection that unifies a wide range of event detection problems, in which the data are assumed to be governed by some underlying statistical distributions, but is data-driven in the sense that little is assumed a priori about the distributions. The developed detection approaches and statistical tools have a wide range of applications, including fraud detection, clinical trials, medical diagnosis, high-frequency trading, voting irregularity analysis, and network intrusion.A comprehensive approach to general network event detection problems is developed in this project through the exploration of three thrusts: (i) detection of (unstructured) point events, (ii) detection of graph-based structured events, and (iii) sequential and quickest detection of dynamically evolving graph structures. The performance of the designed tests is characterized in terms of the probability of detection error and the rate at which this error goes to zero. Various fundamental issues are addressed, including non-i.i.d. data streams, as well as the interplay between network size, event size, sample size, and data dimension.

网络事件检测的目标是检测大型网络上一组节点的存在，这些节点的观察反映了异常事件的发生。现有的网络事件检测研究主要从两个角度进行。第一种是数据驱动的，不假设任何底层统计模型，通常适用于更通用的数据集，但可能不提供性能保证。第二个视角是模型驱动的，假设数据具有某些统计分布（例如高斯分布），并且通常具有性能保证，但可能仅限于数据适合模型的应用程序。该项目的目标是探索一个网络事件检测框架，该框架统一了广泛的事件检测问题，其中假设数据受某些底层统计分布的控制，但从某种意义上说，它是数据驱动的，几乎没有什么可预测的。假设关于分布的先验。所开发的检测方法和统计工具具有广泛的应用，包括欺诈检测、临床试验、医学诊断、高频交易、投票违规分析和网络入侵。本文开发了一种针对一般网络事件检测问题的综合方法。该项目通过探索三个主旨：（i）检测（非结构化）点事件，（ii）检测基于图的结构化事件，以及（iii）动态演变的图结构的顺序和最快检测。所设计的测试的性能以检测错误的概率和该错误变为零的速率来表征。解决了各种基本问题，包括非独立同分布问题。数据流，以及网络大小、事件大小、样本大小和数据维度之间的相互作用。