ADT: i-Group Learning and i-Detect for Dynamic Real Time Anomaly Detection with Applications in Maritime Threat Detection

ADT：用于动态实时异常检测的 i-Group Learning 和 i-Detect 及其在海上威胁检测中的应用

• 批准号: 1737857
• 负责人: Rong Chen
• 金额: $ 30万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737857&HistoricalAwards=false
• 关键词: ADT; Group; Learning; Detect; Dynamic

Threats to national and global security through the maritime transportation system can be multi-faceted and serious, ranging from human and drug trafficking, smuggling, transport of nuclear material and dirty bombs, to garbage dumping and illegal fishing. Hence it is important to design an efficient early detection and risk assessment system for maritime traffic over space and time. With today's data gathering capabilities and global regulation and agreements, it is now possible to achieve such a goal with sophisticated and advanced statistical tools. The project develops novel statistical tools for individualized grouping and baseline distribution formation and for subsequent individualized detection of abnormal deviations from the baseline distribution, with a quantified risk assessment. The new developments are target-oriented and more precise, thus more effective than conventional methods. The project utilizes the Automated Identification System (AIS) data that, by international convention, is transmitted by over a million vessels worldwide, and combines this data with geological, geographical, and geophysical data about oceans and river systems, and coastlines and ports. It provides a timely and effective tool for the improvement of national security. The project also provides a rich training ground for next generation data scientists with interdisciplinary research experiences in maritime security and risk assessment.The project investigates novel statistical approaches for threat detection, utilizing a wide range of data sources. The developed iGroup method and iDetect tools are general statistical methods that form a useful framework for many threat detection and risk assessment problems. They enrich the theory and methods of a new statistical analytical toolkit. The project focuses on utilizing the developed methods in one important application, maritime traffic threat detection and risk assessment. It advances the science of threat detection by developing new tools for dealing with heterogeneous data, extending the data-depth method to complex space, finding new learning tools for handling features that differ in importance, and producing anomaly detection methods for situations where data is missing, incorrect, or deliberately misleading. The development of a real-time threat detection and risk assessment system for maritime traffic can enhance maritime safety and at the same time detect human and group movements on vessels and more generally criminal and terrorist activities of various kinds. The methods to be developed also offer a general framework for threat detection in other areas, such as cell phone monitoring, cyber security, anti-money laundering, market analysis, information retrieval, and personalized medicine. The project also has a strong interdisciplinary educational component for training the next generation of data scientists.

海上运输系统对国家和全球安全的威胁可能是多方面的、严重的，从人口和毒品贩运、走私、核材料和脏弹运输到垃圾倾倒和非法捕鱼。因此，重要的是要设计一个有效的早期检测和风险评估系统的海上交通的空间和时间。凭借今天的数据收集能力以及全球监管和协议，现在有可能利用复杂和先进的统计工具实现这一目标。该项目开发了新的统计工具，用于个性化分组和基线分布形成，以及随后对基线分布的异常偏差进行个性化检测，并进行量化风险评估。 新的发展是有针对性的，更精确，因此比传统方法更有效。该项目利用自动识别系统（AIS）数据，根据国际公约，这些数据由全世界100多万艘船舶传输，并将这些数据与有关海洋和河流系统以及海岸线和港口的地质，地理和地球物理数据相结合。它为改善国家安全提供了及时和有效的工具。该项目还为具有海事安全和风险评估跨学科研究经验的下一代数据科学家提供了丰富的培训基础。该项目利用广泛的数据源，研究了用于威胁检测的新型统计方法。开发的iGroup方法和iDetect工具是通用的统计方法，形成了许多威胁检测和风险评估问题的有用框架。它们丰富了新的统计分析工具包的理论和方法。该项目的重点是在一个重要的应用，海上交通威胁检测和风险评估利用开发的方法。它通过开发用于处理异构数据的新工具，将数据深度方法扩展到复杂空间，找到用于处理重要性不同的特征的新学习工具，以及针对数据丢失，不正确或故意误导的情况产生异常检测方法，来推进威胁检测科学。开发海上交通实时威胁探测和风险评估系统，可以加强海上安全，同时探测船上人员和团体的活动，以及更广泛的各种犯罪和恐怖活动。开发的方法还为其他领域的威胁检测提供了一个通用框架，如手机监控、网络安全、反洗钱、市场分析、信息检索和个性化医疗。该项目还具有强大的跨学科教育组件，用于培训下一代数据科学家。

项目成果

Threshold factor models for high-dimensional time series

• DOI: 10.1016/j.jeconom.2020.01.005
• 发表时间: 2018-09
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Xialu Liu;Rong Chen

Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow

• DOI: 10.6339/22-jds1065
• 发表时间: 2022
• 期刊: Journal of Data Science
• 作者: Elynn Y. Chen;Rong Chen

Individualized Group Learning

• DOI: 10.1080/01621459.2021.1947306
• 发表时间: 2019-06
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Chencheng Cai;Rong Chen;Min‐ge Xie

Leveraging the Fisher Randomization Test using Confidence Distributions: Inference, Combination and Fusion Learning

利用置信分布的 Fisher 随机化检验：推理、组合和融合学习

• DOI: 10.1111/rssb.12429
• 发表时间: 2021
• 期刊: Journal of the Royal Statistical Society Series B: Statistical Methodology
• 作者: Luo, Xiaokang;Dasgupta, Tirthankar;Xie, Minge;Liu, Regina Y.
• 通讯作者: Liu, Regina Y.

iGroup Learning and iDetect for Dynamic Anomaly Detection with Applications in Maritime Threat Detection

iGroup Learning 和 iDetect 用于动态异常检测及其在海上威胁检测中的应用

• DOI: 10.1109/ths.2018.8574162
• 发表时间: 2018
• 期刊: 2018 IEEE International Symposium on Technologies for Homeland Security (HST
• 作者: Cai, Chencheng;Chen, Rong;Liu, Alexander D.;Roberts, Fred S.;Xie, Minge
• 通讯作者: Xie, Minge