ATD: Threat Detection Based on Simultaneous Monitoring of Complex Signals from Multiple Sources

ATD：基于同时监控多源复杂信号的威胁检测

• 批准号: 2123761
• 负责人: Piotr Kokoszka
• 金额: $ 27.58万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123761&HistoricalAwards=false
• 关键词: ATD; Threat; Detection; Based; Simultaneous

The chief question this research addresses is how to utilize information from many sources collectively, rather than from individual sources separately, in order to detect as soon as possible a threat to or disruption of their proper operation. For example, vehicles in a fleet of buses, trucks working in a mine, trains on the move or airplanes in flight emit complex signals with many components describing conditions of their operation. The data that motivate this research have complex structure, large volume, and velocity. The signals consist of many components, which are related in some way, but provide differently structured information and cannot be manipulated by usual algebraic operations. For example, one component may be the altitude of an aircraft, another may be temperature from a sensor placed in an engine, the third may be radiation measurement in the cabin. Complex data streams from a fleet of aircraft in flight must be processed in real time to detect a threat to one, some, or all aircraft. This project aims at developing statistical algorithms to detect a threat in such settings and their numerical implementations. The algorithms will be validated on real data from a fleet of heavy vehicles. However, this research will have a broad applicability as threat detection is crucial in an increasingly connected world consisting of cyber, physical, and human components. It will contribute to workforce development by training several PhD students in research at the intersection of statistics, computer science and engineering. Such expertise is in extremely high demand in private enterprise and government at all levels, from city to federal, as various groups attempt to interrupt the operation of our businesses, infrastructure and government.The state of a number of units being monitored will be quantified as a vector whose entries are complex data structures with non-comparable components. Such an abstract vector is observed at each time instant. The data to be monitored for a threat thus exhibit a complex structure with temporal and cross-sectional dependence. This research will develop algorithms to detect a sudden change in the system. This will be achieved by embedding the entries of the vector introduced above in a metric space, which is practically the most general space in which data can live. Since a metric space generally does not have a vector space structure, which cannot be imposed due to the nature of the data to be processed, the tools that will be developed will open directions of research in time series analysis that will be novel from both the theoretical and practical perspectives. Two classes of algorithms will be considered: 1) algorithms based on a general state space representation, 2) algorithms based on general invariance principles. The generality will be achieved by considering an abstract metric space on which specific conditions demanded by the algorithms will be imposed. The scope of the applicability and reliable performance of the algorithms will be analyzed by mathematical tools, that will lead to precise conditions and assumptions, and by numerical studies that will validate the algorithms on data streams from a fleet of heavy vehicles.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.

这项研究的主要问题是如何共同利用来自多个来源的信息，而不是单独利用来自各个来源的信息，以便尽快发现对其正常运作的威胁或干扰。例如，公交车车队中的车辆、在矿井中工作的卡车、移动中的火车或飞行中的飞机发出复杂的信号，其中许多组件描述了它们的运行条件。推动这项研究的数据结构复杂、体积大、速度快。信号由许多分量组成，这些分量在某种程度上相互关联，但提供了不同结构的信息，不能通过通常的代数运算来处理。例如，一个组件可能是飞机的高度，另一个组件可能是放置在发动机中的传感器的温度，第三个组件可能是机舱内的辐射测量。来自飞行中的飞机机队的复杂数据流必须实时处理，以检测对一架、一些或所有飞机的威胁。该项目旨在开发统计算法，以检测这种环境中的威胁及其数字实施。这些算法将在一支重型车辆车队的真实数据上进行验证。然而，这项研究将具有广泛的适用性，因为在由网络、物理和人类组件组成的日益连接的世界中，威胁检测至关重要。它将通过在统计学、计算机科学和工程的交叉领域培训几名博士生，为劳动力发展做出贡献。随着各种团体试图干扰我们的企业、基础设施和政府的运作，从城市到联邦的各级私营企业和政府对这种专业知识的需求非常高。被监控的多个单位的状态将被量化为一个向量，其条目是具有不可比较成分的复杂数据结构。在每个时刻都会观察到这样的抽象向量。因此，要监测威胁的数据呈现出一种具有时间和横截面相关性的复杂结构。这项研究将开发检测系统突然变化的算法。这将通过将上面介绍的向量的条目嵌入到度量空间中来实现，该度量空间实际上是可以存储数据的最一般的空间。由于度量空间通常不具有向量空间结构，而由于要处理的数据的性质，不能强加向量空间结构，因此将开发的工具将开启时间序列分析的研究方向，从理论和实践角度都将是新颖的。将考虑两类算法：1)基于一般状态空间表示的算法，2)基于一般不变性原理的算法。通用性将通过考虑抽象度量空间来实现，在该抽象度量空间上将施加算法所要求的特定条件。算法的适用范围和可靠性能将通过数学工具进行分析，这将导致精确的条件和假设，并将通过数值研究来验证来自重型车辆车队的数据流上的算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Testing normality of data on a multivariate grid

测试多元网格上数据的正态性

• DOI: 10.1016/j.jmva.2020.104640
• 发表时间: 2020
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Horváth, Lajos;Kokoszka, Piotr;Wang, Shixuan
• 通讯作者: Wang, Shixuan

Modeling Probability Density Functions as Data Objects

• DOI: 10.1016/j.ecosta.2021.04.004
• 发表时间: 2021-05
• 期刊: Econometrics and Statistics
• 影响因子: 1.9
• 作者: Alexander Petersen;Chao Zhang;P. Kokoszka

MONITORING FOR A CHANGE POINT IN A SEQUENCE OF DISTRIBUTIONS

• DOI: 10.1214/20-aos2036
• 发表时间: 2021-08-01
• 期刊: ANNALS OF STATISTICS
• 影响因子: 4.5
• 作者: Horvath, Lajos;Kokoszka, Piotr;Wang, Shixuan
• 通讯作者: Wang, Shixuan

The intrinsic dimensionality of network datasets and its applications1

网络数据集的内在维数及其应用1

• DOI: 10.3233/jcs-220131
• 发表时间: 2023
• 期刊: Journal of Computer Security
• 影响因子: 1.2
• 作者: Gorbett, Matt;Siebert, Caspian;Shirazi, Hossein;Ray, Indrakshi
• 通讯作者: Ray, Indrakshi

Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment

• DOI: 10.1109/bigdata59044.2023.10386280
• 发表时间: 2023-11
• 期刊: 2023 IEEE International Conference on Big Data (BigData)
• 作者: Matt Gorbett;Hossein Shirazi;Indrakshi Ray