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

• 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

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，因此任何提高威胁检测算法性能的尝试都可能导致双重努力，以设计更强大的反威胁检测技术，从而留下更少的证据。在该项目中，将开发一个博弈论框架来研究对抗性网络环境中最快威胁检测的双重努力的最终极限。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，激发原创研究思想的发展，并促进跨学科合作。研究人员致力于扩大研究生和本科生中代表性不足的少数族裔和女性对 STEM 教育的参与。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主题的新课程。该项目有望在最快的变化检测、对抗性学习、序列分析和博弈论方面做出新的贡献。将开发一种开发纳什均衡策略的系统方法，以在网络对抗环境中实现最快的威胁检测，并从理论上描述其在纳什均衡下的基本性能限制。该项目由三个重点组成。第一个重点关注具有时间结构的对抗性攻击下的一个数据流。第二个重点关注具有多个独立数据流的情况。第三个重点是具有图形相关结构的网络。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

使用未标记样本最快地检测传感器网络异常

• DOI: 10.1109/tsp.2023.3256275
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Sun, Zhongchang;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Adversarially robust sequential hypothesis testing

对抗性稳健序贯假设检验

• DOI: 10.1080/07474946.2022.2043050
• 发表时间: 2022-01
• 期刊: Sequential Analysis
• 作者: Cao, Shuchen;Zhang, Ruizhi;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Data-Driven Quickest Change Detection in Markov Models

马尔可夫模型中数据驱动的最快变化检测

• DOI: 10.1109/icassp49357.2023.10096555
• 发表时间: 2023-06
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Kernel Robust Hypothesis Testing

内核稳健假设检验

• DOI: 10.1109/tit.2023.3268207
• 发表时间: 2023-04
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Sun, Zhongchang;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Robust Hypothesis Testing With Moment Constrained Uncertainty Sets

具有矩约束不确定性集的稳健假设检验

• DOI: 10.1109/icassp49357.2023.10096843
• 发表时间: 2023-06
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Magesh, Akshayaa;Sun, Zhongchang;Veeravalli, Venugopal V.;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng