SaTC: CORE: Medium: Physically Unclonable Wireless Systems (PUWS) for RF Fingerprinting and Physical Layer Security

SaTC：核心：中：用于射频指纹识别和物理层安全的物理不可克隆无线系统 (PUWS)

基本信息

批准号：
2233774
负责人：
Gokhan Mumcu
金额：
$ 120万
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：
2023
资助国家：
美国
起止时间：
2023-02-15 至 2027-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233774&HistoricalAwards=false
关键词：
SaTC CORE Medium Physically Unclonable

项目摘要

The goal of this project is to investigate wireless communication systems utilizing a new chaotic antenna array that includes intentional but unclonable randomizations in its geometry. Due to these randomizations, the wireless signals transmitted by the chaotic antenna arrays exhibit unique and strongly distinct fingerprints that can be utilized by the wireless communication systems to ensure security in emerging applications such as the Internet of Things. The enhanced fingerprints of chaotic antenna arrays will be harnessed for two purposes: authenticating wireless devices for joining a wireless network, and reducing the eavesdroppers’ capability to understand/interpret wireless signals transmitted between a legitimate user and an access point. The authentication and security methods proposed in this project are hardware-based and complement the existing software-based methods such as passwords and cryptography to achieve stronger security measures in wireless systems. The project will develop various configurations of chaotic antenna arrays, investigate their performance for authentication, wireless communication security, and data rate. Machine learning will be employed to achieve accurate and resilient authentication. Chaotic antenna arrays are proposed to introduce antenna element specific phase errors with spatial variation, leveraging the flexibilities of the mask-free laser-enhanced direct print additive manufacturing technique. The proposed array architecture, without knowledge of its own errors, will provide dual support for radio-frequency fingerprinting based authentication and physical layer security. Machine learning methods, in particular deep neural networks, will be used to perform authentication using the enhanced radio-frequency fingerprints, which should yield significantly greater accuracy compared to using radio frequency fingerprints of regular antenna arrays. To mathematically analyze the improvement in the authentication capacity, information theoretic bounds for authentication success probability and attack success probability will be studied. Possible adversarial machine learning attacks to chaotic antenna arrays and defense strategies will be also investigated. Finally, from the wireless communications perspective, the achievable data rate and the security improvements against eavesdroppers will be analyzed considering the enhanced wireless channel diversity and more challenging channel estimation conditions for eavesdroppers, respectively.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.

该项目的目的是利用新的混沌天线阵列研究无线通信系统，该阵列包括故意但无统治的Rantyiza- 在其几何形状中。由于这些随机性，混乱天线阵列传输的无线信号暴露了独特且截然不同的指纹，无线通信系统可以利用这些指纹，以确保在物联网等新兴应用程序中的安全性。混沌天线阵列的增强指纹将用于两个目的：验证无线设备，用于连接无线网络，并降低窃听者在合法用户和访问点之间传播的窃听者的能力。该项目中提出的身份验证和安全方法基于硬件，并补充现有的基于软件的方法，例如密码和加密术，以实现无线系统中的更强安全措施。该项目将开发混乱天线阵列的各种配置，研究其对身份验证，无线通信安全性和数据速率的性能。机器学习将被雇用以实现准确和抗性的身份验证。提出了混沌天线阵列，以引入天线元件特定相误差，并通过空间变化，利用无面膜激光增强的直接印刷添加剂制造技术的灵活性。所提出的阵列体系结构在不了解其自身错误的情况下将为基于射频指纹的身份验证和物理层安全性提供双重支持。机器学习方法，尤其是深神经网络，将使用增强的射频指纹进行身份验证，与使用常规天线阵列的射频指纹相比，该方法应具有明显更高的准确性。为了数学分析身份验证能力的提高，将研究认证成功概率和攻击成功概率的信息理论界限。还将研究对混乱天线阵列和防御策略的对抗机器学习攻击。最后，从无线通信的角度来看，将分析可实现的数据速率和针对窃听器的安全性提高，考虑到增强的无线渠道多样性和更具挑战性的渠道估计条件，这是NSF的立法任务，并通过基金会的智力综述和广泛的评论来诚实地评估了NSF的立法任务。