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

这个项目的目标是研究利用一种新的混沌天线阵列的无线通信系统，该阵列在其几何结构中包含有意但不可克隆的随机化。由于这些随机化，由混沌天线阵列发射的无线信号显示出独特的和强烈不同的指纹，无线通信系统可以利用这些指纹来确保诸如物联网等新兴应用的安全性。混沌天线阵列的增强指纹将用于两个目的：验证加入无线网络的无线设备，以及降低窃听者理解/解释合法用户和接入点之间传输的无线信号的能力。本项目中提出的认证和安全方法是基于硬件的，并补充了现有的基于软件的方法，如密码和密码学，以实现更强的无线系统安全措施。该项目将开发各种配置的混沌天线阵列，调查其在认证、无线通信安全性和数据速率方面的性能。将使用机器学习来实现准确和有弹性的身份验证。利用无掩模激光增强直接印刷添加剂制造技术的灵活性，提出了混沌天线阵列来引入天线单元特定的随空间变化的相位误差。建议的阵列体系结构在不知道自身错误的情况下，将为基于射频指纹的身份验证和物理层安全提供双重支持。将使用机器学习方法，特别是深度神经网络，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，这应该会产生更高的准确性。为了从数学上分析认证能力的提高，我们将研究认证成功概率和攻击成功概率的信息论界。还将调查针对混沌天线阵列的可能的对抗性机器学习攻击和防御策略。最后，从无线通信的角度，将分别考虑增强的无线信道分集和对窃听者更具挑战性的信道估计条件，分析可实现的数据速率和针对窃听者的安全改进。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.