权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
1737962
负责人：
Georgios Fellouris
金额：
$ 13.1万
依托单位：
University of Illinois at Urbana-Champaign
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 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737962&HistoricalAwards=false
关键词：
ATD Collaborative Research Efficient sampling

项目摘要

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，可以几乎实时地观察到，但物理限制和预算限制需要对这些数据流进行有效的采样。将这些限制纳入威胁检测系统的设计中，有可能大大节省资源。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、实时的抽样，并随后发现、确定和预防不同性质的威胁，如恐怖主义活动和信贷欺诈。开发的方法将在真实世界的数据上进行测试，在危机时期，将使用手机通话记录发现潜在的社区结构。将在这个项目中获得的理论知识将被纳入研究生水平课程的材料中，这些课程涵盖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。该项目将尽一切努力将符合条件的代表不足群体的学生纳入这些研究活动。本研究将解决两个基本研究问题：1)如何实时检测和识别受采样约束的网络数据中的异常簇；2)如何有效地分配有限的资源，以延迟或防止来自已识别的异常簇的威胁的实现。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序列检测的新问题，其解决方案需要来自不同领域的工具的创造性组合，如统计推理、序列分析和信息论。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，例如将根据手机通话数据发现的危机时期的社交网络。总体而言，这是一项跨越社会科学、统计学和工程学的多学科提案，其目标是在强大的理论背景基础上获得大量有效的网络采样方案和新颖的威胁检测算法。