Collaborative Research: SWIFT: SMALL: Understanding and Combating Adversarial Spectrum Learning towards Spectrum-Efficient Wireless Networking

合作研究：SWIFT：SMALL：理解和对抗对抗性频谱学习以实现频谱高效的无线网络

• 批准号: 2029858
• 负责人: Jie Xu
• 金额: $ 18.2万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029858&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; SMALL; Understanding

The next-generation wireless network is expected to be efficient, reliable, secure and spectrum-intelligent to maximize the efficiency of using the wireless spectrum. Dynamic spectrum access and management designs have shown their potential to substantially improve the spectrum utilization efficiency. To achieve the goal of securing efficient spectrum access and utilization, it is common in wireless network systems to collect and use spectrum reports from individual nodes to detect malicious behaviors and/or eliminate attack impacts. The goal of this project is to understand the potential strategies and impacts of a new attack, called adversarial spectrum learning, which aims to learn from the wireless spectrum data and construct specific attack models against spectrum management systems to disrupt the network performance. The project will also provide effective countermeasure against adversarial spectrum learning.The project will focus on studying important research problems associated with adversarial spectrum learning via analytical modeling, comprehensive simulations, and experimental evaluations. Specifically, the research team aims at (i) formulating the adversarial spectrum learning strategies that can be used by malicious attacks to disrupt the spectrum efficiency and evaluating their damaging impacts in varying wireless and network conditions; (ii) detecting adversarial spectrum learning attacks via new non-parametric detection methods; (iii) creating attack prevention and secure management methods for secure and efficient spectrum access in the potential presence of adversarial spectrum learning, and (iv) comprehensively evaluating the efficiency and effectiveness of the proposed detection and prevention strategies. Appropriate elements from the project will also be integrated into educational materials.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.

下一代无线网络被期望是高效、可靠、安全和频谱智能的，以最大限度地提高无线频谱的使用效率。动态频谱接入和管理设计已经显示出它们的潜力，以大大提高频谱利用效率。为了实现确保有效频谱接入和利用的目标，在无线网络系统中通常收集和使用来自各个节点的频谱报告来检测恶意行为和/或消除攻击影响。该项目的目标是了解一种新攻击的潜在策略和影响，称为对抗性频谱学习，旨在从无线频谱数据中学习并构建针对频谱管理系统的特定攻击模型，以破坏网络性能。该项目还将提供对抗性频谱学习的有效对策。该项目将通过分析建模、综合模拟和实验评估，重点研究与对抗性频谱学习相关的重要研究问题。具体而言，研究团队的目标是（i）制定可被恶意攻击用来破坏频谱效率的对抗性频谱学习策略，并评估其在不同无线和网络条件下的破坏性影响;（ii）通过新的非参数检测方法检测对抗性频谱学习攻击;（iii）创建攻击预防和安全管理方法，用于在对抗性频谱学习的潜在存在下安全和有效的频谱接入，及（iv）全面评估建议的侦测及预防策略的效率及成效。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

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

• DOI: 10.1109/twc.2021.3113346
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Jie Xu;Heqiang Wang;Lixing Chen

Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing

协作频谱感知中对抗性机器学习攻击的低成本影响限制防御

• DOI: 10.1145/3468218.3469051
• 发表时间: 2021
• 期刊: WiseML '21: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning
• 作者: Luo, Zhengping;Zhao, Shangqing;Duan, Rui;Lu, Zhuo;Sagduyu, Yalin E.;Xu, Jie
• 通讯作者: Xu, Jie

Learning the Optimal Partition for Collaborative DNN Training with Privacy Requirements

学习具有隐私要求的协作 DNN 训练的最佳划分

• DOI: 10.1109/jiot.2021.3127715
• 发表时间: 2021
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Zhang, Letian;Xu, Jie
• 通讯作者: Xu, Jie

Fooling Edge Computation Offloading via Stealthy Interference Attack

通过隐形干扰攻击欺骗边缘计算卸载

• DOI: 10.1109/sec50012.2020.00062
• 发表时间: 2020
• 期刊: 2020 IEEE/ACM Symposium on Edge Computing (SEC
• 作者: Zhang, Letian;Xu, Jie
• 通讯作者: Xu, Jie

When Attackers Meet AI: Learning-Empowered Attacks in Cooperative Spectrum Sensing

• DOI: 10.1109/tmc.2020.3030061
• 发表时间: 2022-05-01
• 期刊: IEEE TRANSACTIONS ON MOBILE COMPUTING
• 影响因子: 7.9
• 作者: Luo, Zhengping;Zhao, Shangqing;Sagduyu, Yalin E.
• 通讯作者: Sagduyu, Yalin E.