CRII: CIF: Dynamic Network Event Detection with Time-Series Data

CRII：CIF：使用时间序列数据进行动态网络事件检测

• 批准号: 1948165
• 负责人: Shaofeng Zou
• 金额: $ 17.49万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948165&HistoricalAwards=false
• 关键词: CRII; CIF; Dynamic; Network; Event

Network data is ubiquitous in real-world applications, e.g., communication networks, social networks, computer networks, sensor networks, power grids, World Wide Web, and Internet of Things. In these applications, the collected data are usually associated with a network structure, and the network structure may correspond to communication links in wireless networks, information flows in social networks, and causal relationships between the network nodes. Compared to individual entities, network data, by their interconnected nature, contain rich causal and correlation information, and present both complication and opportunity for statistical inference. Timely detection of dynamic events as soon as they occur is a problem of great interest, especially as false alarms are possible in networks of really large sizes, and measurements taken from one part of the network may not accurately capture events in another part of the network. This project addresses this challenge and develops a comprehensive framework for dynamic event detection in networks with time-series data. The developed methodology in this project can benefit a wide range of applications, e.g., intrusion detection in computer networks, epidemic detection, seismic event detection, and fake news detection in social networks. This project will substantially advance the understanding of how to accurately model and sequentially detect an event with a dynamic nature, and how to exploit network topology for reliable and computationally efficient detection in networks. In the project, the following two thrusts will be explored: (i) detection of dynamics at a single node; and (ii) detection of dynamics in the network. Practical applications, e.g., fault detection in electric motor, dynamic community detection in social networks and seismic event detection, will be studied to validate algorithms developed in this project. Tools from probability theory, information theory and stochastic optimization will be used to develop novel methodologies that address the underlying challenges in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络数据在现实世界的应用中无处不在，例如通信网络、社交网络、计算机网络、传感器网络、电网、万维网和物联网。在这些应用中，收集到的数据通常与网络结构相关联，该网络结构可以对应于无线网络中的通信链路、社交网络中的信息流以及网络节点之间的因果关系。与单个实体相比，网络数据由于其相互关联的性质，包含丰富的因果和相关信息，并为统计推断提供了复杂性和机会。动态事件一发生就及时检测是一个令人非常感兴趣的问题，特别是在规模非常大的网络中可能出现误报，并且从网络的一个部分进行的测量可能无法准确捕获网络另一部分的事件。该项目解决了这一挑战，并开发了一个用于时间序列数据网络中动态事件检测的综合框架。该项目开发的方法可以使广泛的应用受益，例如计算机网络中的入侵检测、流行病检测、地震事件检测和社交网络中的假新闻检测。该项目将极大地促进人们对如何准确建模和顺序检测具有动态性质的事件，以及如何利用网络拓扑在网络中进行可靠且计算高效的检测的理解。在该项目中，将探索以下两个重点：（i）单个节点的动态检测； (ii) 网络动态检测。将研究实际应用，例如电动机故障检测、社交网络中的动态社区检测和地震事件检测，以验证该项目中开发的算法。概率论、信息论和随机优化的工具将用于开发解决该项目中潜在挑战的新颖方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

Quickest Detection of Series Arc Faults on DC Microgrids

直流微电网串联电弧故障的最快检测

• DOI: 0.1109/ecce47101.2021.9595315
• 发表时间: 2021-10
• 期刊: 2021 IEEE Energy Conversion Congress and Exposition (ECCE
• 作者: Kaushik Gajula;Vu Le;Xiu Yao;Shaofeng Zou;Luis Herrera
• 通讯作者: Luis Herrera

A Game-Theoretic Approach to Sequential Detection in Adversarial Environments

对抗环境中顺序检测的博弈论方法

• DOI: 10.1109/isit44484.2020.9173965
• 发表时间: 2020-06
• 期刊: 2020 IEEE International Symposium on Information Theory (ISIT
• 作者: Zhang, Ruizhi;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation

一般光滑函数逼近的两个时间尺度TDC的非渐近分析

• DOI: 10.1142/s0218127419300179
• 发表时间: 2021-04-07
• 期刊: Int. J. Bifurc. Chaos
• 作者: Yue Wang;Shaofeng Zou;Yi Zhou
• 通讯作者: Yi Zhou

Data-Driven Quickest Change Detection in Hidden Markov Models

隐马尔可夫模型中数据驱动的最快变化检测

• DOI: 10.1109/isit54713.2023.10206588
• 发表时间: 2023-06
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT
• 作者: Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

使用未标记样本最快地检测传感器网络异常

• DOI: 10.1109/tsp.2023.3256275
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Sun, Zhongchang;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng