Collaborative Research: CNS Core: Small: Closing the Theory-Practice Gap in Understanding and Combating Epidemic Spreading on Resource-Constrained Large-Scale Networks

合作研究：CNS核心：小型：缩小理解和抗击资源有限的大规模网络上的流行病传播的理论与实践差距

• 批准号: 2209922
• 负责人: Chul-Ho Lee
• 金额: $ 25万
• 依托单位: Texas State University - San Marcos
• 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: 2021-11-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209922&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

There has been an explosive growth in the number of Internet-connected devices. The end-device users have also built a stack of rich and complex networks, derived from their social, personal and work groups. The prolific connections to end-devices and users, however, can be exploited as devastating vehicles for malware and worm attacks. Since exploiting the network connectivity lies at the heart of malware distribution, it becomes crucial to understand how the underlying network structure affects the malware propagation. Despite abundant literature on epidemic modeling and analysis, there is still a huge gap between theory and practice. This project aims to bridge the gap to better understand and combat epidemic spreading on large-scale networks with realistic cost constraints. This collaborative project brings together investigators from Texas State University and North Carolina State University to investigate the following inter-related research thrusts. It will (1) develop a theoretical framework to fully characterize the transient dynamics of epidemic spreading on a general graph (as opposed to a complete graph) to estimate and predict the likelihood of each node being infected for the future time, (2) develop a suite of readily usable algorithms to mitigate the spread of an epidemic to the extent possible under realistic constraints, and (3) develop a set of algorithms for efficient estimation and inference of network and epidemic parameters from incomplete and noisy data of epidemic cascades. This project could potentially have a high impact on a vast range of multi-disciplinary areas and applications where the study of epidemics has been necessary and crucial, including epidemiology, percolation in physics and chemistry, rumor spreading, information cascades, viral marketing, and spread of misinformation and fake news. In addition, this project will integrate research findings into education by curriculum development, involve diverse undergraduate and graduate students, especially women and students of underrepresented groups, and have them trained to thrive and contribute to the society in industrial and academic settings after graduation. All products developed during the course of this project will be publicly available and hosted at https://sites.google.com/view/nsf-cns-eun-lee-epidemic for at least three years after the closing of the project. 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.

项目成果

Controlling Epidemic Spread Under Immunization Delay Constraints

在免疫延迟限制下控制疫情蔓延

• 发表时间: 2023
• 期刊: IFIP Networking 2023
• 作者: Li, Shiju;Huang, Xin;Lee, Chul-Ho Lee;Eun, Do Young
• 通讯作者: Eun, Do Young