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: 2029875
• 负责人: Zhuo Lu
• 金额: $ 27万
• 依托单位: University of South Florida
• 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=2029875&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.

预计下一代无线网络有效，可靠，安全和频谱智能，以最大程度地提高使用无线频谱的效率。动态频谱访问和管理设计表明了它们的潜力，可以大大提高光谱利用率。为了实现确保有效频谱访问和利用率的目标，在无线网络系统中很常见地收集和使用各个节点的频谱报告来检测恶意行为和/或消除攻击影响。该项目的目的是了解新攻击的潜在策略和影响，称为对抗频谱学习，该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。该项目还将为对抗性谱学习提供有效的对策。该项目将着重于通过分析建模，综合模拟和实验评估来研究与对抗光谱学习相关的重要研究问题。具体而言，研究小组的目的是（i）制定恶意攻击可以使用的对抗光谱学习策略，以破坏频谱效率并评估其在不同的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光谱学习攻击； （iii）创建预防攻击和安全的管理方法，以在潜在的对抗光谱学习的存在下进行安全有效的光谱访问，以及（iv）全面评估拟议的检测和预防策略的效率和有效性。该项目的适当要素还将集成到教育材料中。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估的。

项目成果

MUSTER: Subverting User Selection in MU-MIMO Networks

• DOI: 10.1109/infocom48880.2022.9796815
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Tao Hou;Shen Bi;Tao Wang;Zhuo Lu;Yao-Hong Liu;S. Misra;Y. Sagduyu

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

• DOI: 10.1109/cvprw56347.2022.00034
• 发表时间: 2022-04
• 期刊: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 作者: Furkan Mumcu;Keval Doshi;Yasin Yılmaz

IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification

• DOI: 10.1109/dyspan53946.2021.9677264
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
• 作者: Tao Hou;Tao Wang;Zhuo Lu;Yao-Hong Liu;Y. Sagduyu

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.

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

• DOI: 10.1109/jiot.2021.3091674
• 发表时间: 2022-01-01
• 期刊: IEEE INTERNET OF THINGS JOURNAL
• 影响因子: 10.6
• 作者: Nassar, Almuthanna;Yilmaz, Yasin
• 通讯作者: Yilmaz, Yasin