ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection

ATD：扫描动态时空离散事件以进行威胁检测

• 批准号: 1830210
• 负责人: Yao Xie
• 金额: $ 27.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830210&HistoricalAwards=false
• 关键词: ATD; Scanning; Dynamic; Spatial; Temporal

The overarching research objective of this project is to develop a statistical framework for detecting anomalies from spatial-temporal discrete event data. Nowadays, a large volume of such event data dispersed over space and time are becoming increasingly available in a wide variety of applications, such as human activity data, social network data, and crime data. The observations of the discrete events can occur in continuous time and locations, and there can be a complex text description of such events. The discrete event data contain rich correlation and causal information, which can potentially be used to infer the dynamics of the underlying systems and detect threats. The project aims to develop statistical methods to harvest this potential in threat detection using discrete events and address the algorithmic and computational challenges. The developed methods will go beyond the status-quo model estimation by considering more general statistical inference problems such as hypothesis tests and likelihood-based inference. The developed methods are general and can be used for various discrete event data. The project will specifically demonstrate the effectiveness of the developed methods on a large-scale crime dataset collected by the Atlanta Police Department. Recently, point process models have been proven an effective model for capturing the correlation structure in discrete events. While much success has been achieved in estimating the self-exciting spatial-temporal point process models, it remains unclear how one can perform anomaly detection leveraging these models, since (1) detection (which can be cast as hypothesis test) is inherently different from estimation, which involves different kinds of statistics and performance metrics; (2) in various situations, there is a large number of discrete events over broad spatial areas, and the goal is to detect a small cluster of related events, which amounts to "finding a needle in a haystack", thus there is a need to develop powerful and computationally efficient statistics; (3) the normal or reference state can be complex and dynamic and methods need to adapt to the slowly time-varying normal state. The project will address these challenges and provide answers to two related fundamental questions: how to detect clusters of correlated events from a large amount of data using the point process model, and how to estimate time-varying background normal pattern. The proposed work will advance the state-of-art for scan statistic research and build a novel connection between pseudo-likelihood estimation and reinforcement learning. The developed methods will be tested in a specific application of crime data analysis. The proposed education activities will involve students at all levels in rigorous mathematical and statistical training and gain hands-on data analysis skills.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.

该项目的总体研究目标是开发一个统计框架，用于从时空离散事件数据中检测异常。如今，在各种应用程序中，在人类活动数据，社交网络数据和犯罪数据等广泛的应用程序中，大量的此类事件数据越来越多。离散事件的观察可能会在连续的时间和位置发生，并且可以对此类事件进行复杂的文本描述。离散的事件数据包含丰富的相关性和因果信息，可以使用这些信息来推断基础系统的动态并检测威胁。该项目旨在开发统计方法，以使用离散事件来收获威胁检测的潜力，并解决算法和计算挑战。开发的方法将通过考虑更一般的统计推断问题，例如假设检验和基于可能性的推断，超越了现状模型估计。开发的方法是一般的，可用于各种离散事件数据。该项目将特别证明开发方法对亚特兰大警察局收集的大规模犯罪数据集的有效性。最近，已证明点过程模型是捕获离散事件中相关结构的有效模型。尽管在估计自我兴奋的空间 - 周期点过程模型方面已经取得了很大的成功，但仍不清楚如何执行这些模型的异常检测，因为（1）检测（可以作为假设检测施放为假设检测）与估计固有不同，这涉及不同类型的统计量和性能指标； （2）在各种情况下，在广泛的空间区域上存在许多离散事件，目标是检测一小部分相关事件，这相当于“在干草堆中找到针头”，因此需要开发强大而计算上有效的统计数据； （3）正常状态或参考状态可能是复杂且动态的，并且需要适应缓慢的时变正常状态。该项目将解决这些挑战，并为两个相关的基本问题提供答案：如何使用点过程模型从大量数据中检测相关事件的群体，以及如何估计时间变化的背景正常模式。拟议的工作将推动扫描统计研究的最新工作，并在伪样估计与强化学习之间建立新的联系。开发的方法将在犯罪数据分析的特定应用中进行测试。拟议的教育活动将涉及各个级别的学生进行严格的数学和统计培训，并获得动手数据分析技能。该奖项反映了NSF的法定任务，并使用基金会的知识绩效和更广泛的影响审查标准，认为值得通过评估来获得支持。

项目成果

Tensor Kernel Recovery for Discrete Spatio-Temporal Hawkes Processes

离散时空霍克斯过程的张量核恢复

• DOI: 10.1109/tsp.2022.3229642
• 发表时间: 2022
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Sheen, Heejune;Zhu, Xiaonan;Xie, Yao
• 通讯作者: Xie, Yao

Crime Event Embedding with Unsupervised Feature Selection

• DOI: 10.1109/icassp.2019.8682285
• 发表时间: 2018-06
• 期刊: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Shixiang Zhu;Yao Xie

Sequential Change-Point Detection for Mutually Exciting Point Processes

• DOI: 10.1080/00401706.2022.2054862
• 发表时间: 2021-02
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Haoyun Wang;Liyan Xie;Yao Xie;Alex Cuozzo;Simon Mak

Conformal prediction interval for dynamic time-series

• 发表时间: 2020-10
• 作者: Chen Xu;Yao Xie

Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling

• DOI: 10.1109/tits.2021.3068139
• 发表时间: 2020-05
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Shixiang Zhu;Ruyi Ding;Minghe Zhang;P. V. Hentenryck;Yao Xie