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.

随着无线通信和高级传感技术的近期发展，可用于广泛的威胁检测应用程序，例如入侵检测，异常检测，伪造新闻检测和虚假数据注射检测，可以提供丰富而复杂的高维数据。但是，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的影响，例如测量操作和虚假数据注入。此外，威胁通常是由人为因素引起的，因此，任何改善威胁检测算法的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，而留下更少的证据。在该项目中，将开发一个游戏理论框架，以研究在对抗网络环境中最快威胁检测双重努力的最终限制。研究人员将在会议，研讨会和座谈会上共同组织有关最快变化检测的特殊会议，以传播该项目的研究成果，正式化了深远的研究方向，确定了该散发性领域的新挑战，刺激了原始研究思想的发展，并促进了学科的跨学科协作。调查人员致力于扩大代表性不足的少数民族和妇女的参与，包括研究生和本科生在STEM教育中的参与。调查人员将丰富他们当前的课程，并进一步开发与该项目相关的主题的新课程。预计该项目将为最快的变更检测，对抗性学习，顺序分析和游戏理论做出新的贡献。将开发一种系统的系统方法，以开发NASH平衡策略，以在网络对抗环境中进行最快的威胁检测，并且将在理论上表征其NASH平衡的基本性能限制。该项目由三个推力组成。第一个推力集中在具有时间结构的对抗攻击下的一个数据流。第二个推力集中在具有多个独立数据流的情况下。第三个推力重点关注具有图形相关结构的网络。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。

项目成果

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
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Magesh, Akshayaa;Sun, Zhongchang;Veeravalli, Venugopal V.;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Quickest Change Detection in Anonymous Heterogeneous Sensor Networks

匿名异构传感器网络中最快的变化检测

• DOI: 10.1109/tsp.2022.3148535
• 发表时间: 2022
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Sun, Zhongchang;Zou, Shaofeng;Zhang, Ruizhi;Li, Qunwei
• 通讯作者: Li, Qunwei

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
• 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

Data-Driven Quickest Change Detection in Hidden Markov Models

隐马尔可夫模型中数据驱动的最快变化检测

• DOI: 10.1109/isit54713.2023.10206588
• 发表时间: 2023
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT
• 作者: Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;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
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng