ATD: Spatio-Temporal Model for the Propagation of Internet Traffic Anomalies

ATD：互联网流量异常传播的时空模型

• 批准号: 1737795
• 负责人: Piotr Kokoszka
• 金额: $ 20万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737795&HistoricalAwards=false
• 关键词: ATD; Spatio; Temporal; Model; Propagation

This project seeks to develop a statistical model for the propagation of internet traffic anomalies. The model will serve as a tool in an on-going, multidirectional effort aimed at increasing the security of the backbone internet network in the United States and the detection of threats to its operation caused either by suboptimal design or malicious attacks. The model will be constructed using a large publicly available data set of various internet traffic measurements. The work will involve statistical analysis of the data, probabilistic modeling, and simulation. The research will combine expertise of statistics and computer science researchers. By involving Ph.D. students in the field at the nexus of statistics and computer networks, it will train highly educated personnel in an area of national importance.While modeling normal network traffic has received a great deal of attention in the last twenty years, only certain local aspects of stochastic modeling of anomalous behavior have been addressed. Normal traffic models have been used to extract anomalies, but they do not provide information on the statistical properties of the propagation and size of anomalies, nor do they imply a stochastic mechanism that may be used to simulate the flow of anomalies. Developing a stochastic model for the propagation of network anomalies requires a new synthesis of statistical spatio-temporal modeling and discrete event simulation techniques. Spatio-temporal models currently used in various applications including industrial mining, geophysical, climate and environmental research, and public health are not transferable to the setting of internet traffic, where physical distances play no role, while network topology and link utilization become prominent. This research aims to create a new class of mathematical models that will open up new directions of research on network anomaly propagation. The models will be based on state-of-the-art statistical analysis applied to practically-relevant anomaly traffic attributes. The work will also stimulate research in the mathematical sciences on models of this type.

该项目旨在开发一个统计模型，用于传播互联网流量异常。该模型将作为一种工具，用于持续的、多方向的努力，旨在提高美国骨干互联网网络的安全性，并检测由次优设计或恶意攻击造成的对其运营的威胁。该模型将使用各种互联网流量测量的大型公开数据集构建。这项工作将涉及数据的统计分析，概率建模和模拟。这项研究将结合统计学和计算机科学研究人员的联合收割机专业知识。通过参与博士学位在统计学和计算机网络的连接领域的学生，它将培养在国家的重要领域受过高等教育的人才。虽然建模正常的网络流量在过去的20年里得到了很大的关注，只有某些局部方面的异常行为的随机建模已经解决。正常的流量模型已被用来提取异常，但它们不提供有关异常的传播和大小的统计特性的信息，也不意味着可以用于模拟异常流的随机机制。开发网络异常传播的随机模型需要一种新的统计时空建模和离散事件仿真技术的综合。目前在各种应用中使用的时空模型，包括工业采矿，地球物理，气候和环境研究，以及公共卫生是不可转移到互联网流量的设置，物理距离不起作用，而网络拓扑结构和链路利用率变得突出。本研究旨在建立一类新的数学模型，为网络异常传播的研究开辟新的方向。这些模型将基于应用于实际相关异常流量属性的最新统计分析。 这项工作还将促进数学科学对这类模型的研究。

项目成果

Semiparametric Modeling with Nonseparable and Nonstationary Spatio-Temporal Covariance Functions and Its Inference

不可分离非平稳时空协方差函数的半参数建模及其推论

• DOI: 10.5705/ss.202016.0297
• 发表时间: 2019
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Chu, Tingjin;Zhu, Jun;Wang, Haonan
• 通讯作者: Wang, Haonan

Non-asymptotic properties of spectral decomposition of large Gram-type matrices and applications

• DOI: 10.3150/21-bej1384
• 发表时间: 2022-05
• 期刊: Bernoulli
• 影响因子: 1.5
• 作者: Lyuou Zhang;Wen Zhou;Haonan Wang

Tests of Normality of Functional Data

• DOI: 10.1111/insr.12362
• 发表时间: 2020-02-17
• 期刊: INTERNATIONAL STATISTICAL REVIEW
• 影响因子: 2
• 作者: Gorecki, Tomasz;Horvath, Lajos;Kokoszka, Piotr
• 通讯作者: Kokoszka, Piotr

Semiparametric method and theory for continuously indexed spatio-temporal processes

连续索引时空过程的半参数方法和理论

• DOI: 10.1016/j.jmva.2021.104735
• 发表时间: 2021
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Liu, Jialuo;Chu, Tingjin;Zhu, Jun;Wang, Haonan
• 通讯作者: Wang, Haonan

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

函数时间序列的频域理论：方差分解和不变性原理

• DOI: 10.3150/20-bej1199
• 发表时间: 2020
• 期刊: Bernoulli
• 影响因子: 1.5
• 作者: Kokoszka, Piotr;Mohammadi Jouzdani, Neda
• 通讯作者: Mohammadi Jouzdani, Neda