ATD: Collaborative Research: Efficient sampling for real-time detection and isolation of threats in networks

ATD：协作研究：实时检测和隔离网络威胁的高效采样

• 批准号: 1737906
• 负责人: Jacob Shapiro
• 金额: $ 8万
• 依托单位: Princeton 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=1737906&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Efficient; sampling

While modern technologies generate multiple, interconnected sources of data, which can be observed in nearly real time, physical constraints and budget limitations require efficient sampling of these data streams. Incorporating such constraints in the design of threat detection systems has the potential to lead to significant savings in resources. The goal of this project is to develop fundamental statistical theory and methods for the efficient, real-time sampling of networks, and the subsequent detection, identification and prevention of threats of different nature, such as terrorist activities and credit fraud. The developed methodologies will be tested on real-world data, where the underlying community structure in times of crisis will be discovered using cell-phone call records. The theoretical knowledge that will be gained in this project will be incorporated into the material of graduate-level courses that cover adaptive experimental design and sequential detection. Two graduate students will contribute significantly in this research. The project will make every effort to include qualified students of underrepresented groups in these research activities.This research will address two fundamental research questions: 1) how to detect and identify, in real time, anomalous clusters in network data subject to sampling constraints, and 2) how to efficiently allocate limited resources in order to delay or prevent the realization of threats from the identified anomalous clusters. The mathematical formulation of these questions leads to novel problems in network-based, adaptive experimental design and sequential detection, whose solutions require the creative combination of tools from various fields, such as statistical inference, sequential analysis, and information theory. The theory and methods developed in this work will guide the development of threat detection algorithms and will be tested in concrete applications, such as social networks in times of crisis that will be discovered based on cell-phone call data. Overall, this is a multidisciplinary proposal, spanning social sciences, statistics, and engineering, whose goal is to obtain an arsenal of efficient network sampling schemes and novel threat detection algorithms, grounded on a strong theoretical background.

虽然现代技术产生了多个相互关联的数据源，几乎可以在真实的时间内观察到，但物理限制和预算限制要求对这些数据流进行有效采样。在威胁检测系统的设计中阐明这些约束有可能导致资源的显著节省。该项目的目标是发展基本的统计理论和方法，以便对网络进行有效的实时采样，并随后发现、识别和预防不同性质的威胁，如恐怖活动和信贷欺诈。将在真实世界的数据上测试所开发的方法，其中将使用手机通话记录来发现危机时期的潜在社区结构。将在这个项目中获得的理论知识将被纳入研究生课程的材料，包括自适应实验设计和顺序检测。两名研究生将在这项研究中做出重大贡献。本研究将解决两个基本的研究问题：1）如何真实的实时地检测和识别网络数据中受采样约束的异常集群; 2）如何有效地分配有限的资源，以延迟或防止来自所识别的异常集群的威胁的实现。这些问题的数学表述导致了基于网络的自适应实验设计和顺序检测中的新问题，其解决方案需要来自各个领域的工具的创造性组合，例如统计推断，顺序分析和信息论。在这项工作中开发的理论和方法将指导威胁检测算法的开发，并将在具体应用中进行测试，例如在危机时期的社交网络，将根据手机通话数据进行发现。总的来说，这是一个多学科的建议，跨越社会科学，统计学和工程学，其目标是获得一个有效的网络采样方案和新颖的威胁检测算法的武器库，以强大的理论背景为基础。