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CCSS: Collaborative Research: Quickest Threat Detection in Adversarial Sensor Networks

CCSS：协作研究：对抗性传感器网络中最快的威胁检测

基本信息

批准号：
2112693
负责人：
Shaofeng Zou
金额：
$ 21.7万
依托单位：
SUNY at Buffalo
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：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112693&HistoricalAwards=false
关键词：
CCSS Collaborative Research Quickest Threat

项目摘要

With the recent rapid development of wireless communication and advanced sensing technology, rich and complex sequential high-dimensional data are made available for a wide range of threat detection applications, e.g., intrusion detection, anomaly detection, fake news detection, and false data injection detection. However, the reliance on wireless communication and the sparsely spatial distribution of these networked sensors make them vulnerable to adversarial attacks, such as measurement manipulation and false data injection. Moreover, threats are oftentimes caused by human factors, and thus any attempt to improve the performance of threat detection algorithms may result in a dual effort to devise more powerful counter-threat-detection techniques that leave less evidence. In this project, a game-theoretic framework will be developed to investigate the ultimate limits of the dual efforts for quickest threat detection in adversarial networked environments. The investigators will co-organize special sessions at conferences, workshops, and symposia on quickest change detection to disseminate the research outcomes of this project, formalize far-reaching research directions, identify new challenges in this emerging area, stimulate the development of original research ideas, and foster interdisciplinary collaborations. The investigators are committed to broadening the participation of under-represented minorities and women both among the graduate and undergraduate students in STEM education. The investigators will enrich their current courses and further develop new courses on topics related to this project.The project is expected to make new contributions to quickest change detection, adversarial learning, sequential analysis, and game theory. A systematic methodology of developing Nash equilibrium strategies for quickest threat detection in networked adversarial environments will be developed, and their fundamental performance limits at the Nash equilibrium will be theoretically characterized. This project consists of three thrusts. The first thrust focuses on one data stream under adversarial attacks with temporal structure. The second thrust focuses on the case with multiple independent data streams. The third thrust focuses on networks with graphic correlation structure.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.

随着无线通信和先进传感技术的快速发展，丰富而复杂的序列高维数据可用于广泛的威胁检测应用，例如，入侵检测、异常检测、假新闻检测和虚假数据注入检测。然而，这些网络传感器对无线通信的依赖和稀疏的空间分布使它们容易受到对抗性攻击，例如测量操纵和虚假数据注入。此外，威胁通常是由人为因素引起的，因此，任何试图提高威胁检测算法的性能的尝试都可能导致设计更强大的反威胁检测技术的双重努力，从而留下更少的证据。在这个项目中，将开发一个博弈论框架，以研究在对抗性网络环境中最快的威胁检测的双重努力的极限。研究人员将在会议，研讨会和研讨会上共同组织关于最快变化检测的特别会议，以传播该项目的研究成果，正式确定影响深远的研究方向，确定这一新兴领域的新挑战，刺激原创研究思想的发展，并促进跨学科合作。调查人员致力于扩大在STEM教育的研究生和本科生中代表性不足的少数民族和妇女的参与。研究人员将丰富他们现有的课程，并进一步开发与该项目相关的新课程。该项目预计将在最快变化检测，对抗学习，序列分析和博弈论方面做出新的贡献。一个系统的方法，制定纳什均衡策略，最快的威胁检测在网络对抗环境中，将开发和他们的基本性能限制在纳什均衡的理论特征。该项目包括三个重点。第一个推力集中在一个数据流下的对抗性攻击的时间结构。第二个重点是多个独立数据流的情况。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。