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:
- 金额:$ 21.7万
- 依托单位:
- 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:
- 关键词:
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。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 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:
- 影响因子:5.4
- 作者:Sun, Zhongchang;Zou, Shaofeng;Zhang, Ruizhi;Li, Qunwei
- 通讯作者:Li, Qunwei
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:
- 影响因子:0
- 作者:Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;Zou, Shaofeng
- 通讯作者:Zou, Shaofeng
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:
- 影响因子:5.4
- 作者:Sun, Zhongchang;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:
- 影响因子:0
- 作者:Zhang, Qi;Sun, Zhongchang;Herrera, Luis C.;Zou, Shaofeng
- 通讯作者:Zou, Shaofeng
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Shaofeng Zou其他文献
Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
具有样本复杂性分析的无模型鲁棒强化学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Yudan Wang;Shaofeng Zou;Yue Wang - 通讯作者:
Yue Wang
Near-infrared quantum cutting in Bi3+/Yb3+ co-doped oxyfluoride glasses via cooperative energy transfer for solar cells
Bi3/Yb3共掺杂氟氧化物玻璃的近红外量子切割通过太阳能电池的协同能量转移
- DOI:
10.1016/j.optmat.2014.10.047 - 发表时间:
2014-12 - 期刊:
- 影响因子:3.9
- 作者:
Weirong Wang;Shaofeng Zou;Xiao Lei;Huiping Gao;Yanli Mao* - 通讯作者:
Yanli Mao*
Nonparametric Anomaly Detection and Secure Communication
非参数异常检测和安全通信
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Shaofeng Zou - 通讯作者:
Shaofeng Zou
An Information Theoretic Approach to Secret Sharing
秘密共享的信息论方法
- DOI:
10.1109/tit.2015.2421905 - 发表时间:
2014 - 期刊:
- 影响因子:2.5
- 作者:
Shaofeng Zou;Yingbin Liang;L. Lai;S. Shamai - 通讯作者:
S. Shamai
A kernel-based nonparametric test for anomaly detection over line networks
用于线路网络异常检测的基于内核的非参数测试
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Shaofeng Zou;Yingbin Liang;H. Poor - 通讯作者:
H. Poor
Shaofeng Zou的其他文献
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{{ truncateString('Shaofeng Zou', 18)}}的其他基金
CAREER: Robust Reinforcement Learning Under Model Uncertainty: Algorithms and Fundamental Limits
职业:模型不确定性下的鲁棒强化学习:算法和基本限制
- 批准号:
2337375 - 财政年份:2024
- 资助金额:
$ 21.7万 - 项目类别:
Continuing Grant
Collaborative Research: CIF: Medium: Emerging Directions in Robust Learning and Inference
协作研究:CIF:媒介:稳健学习和推理的新兴方向
- 批准号:
2106560 - 财政年份:2021
- 资助金额:
$ 21.7万 - 项目类别:
Continuing Grant
CRII: CIF: Dynamic Network Event Detection with Time-Series Data
CRII:CIF:使用时间序列数据进行动态网络事件检测
- 批准号:
1948165 - 财政年份:2020
- 资助金额:
$ 21.7万 - 项目类别:
Standard Grant
CIF: Small: Reinforcement Learning with Function Approximation: Convergent Algorithms and Finite-sample Analysis
CIF:小型:带有函数逼近的强化学习:收敛算法和有限样本分析
- 批准号:
2007783 - 财政年份:2020
- 资助金额:
$ 21.7万 - 项目类别:
Standard Grant
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