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

这个计画的目标是研究利用一种新的混沌天线阵列的无线通讯系统，这种阵列在其几何形状上包含有意但不可复制的随机化。由于这些随机化，由混沌天线阵列发射的无线信号表现出独特且强烈不同的指纹，无线通信系统可以利用这些指纹来确保诸如物联网之类的新兴应用中的安全性。混沌天线阵列的增强指纹将用于两个目的：验证无线设备加入无线网络，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。本项目建议的认证和安全方法是基于硬件的，并补充现有的基于软件的方法，如密码和加密，以实现无线系统中更强大的安全措施。该项目将开发各种配置的混沌天线阵列，研究它们在认证、无线通信安全和数据速率方面的性能。机器学习将用于实现准确和弹性的身份验证。混沌天线阵列被提出来引入具有空间变化的天线元件特定相位误差，从而利用无掩模激光增强直接打印增材制造技术的灵活性。所提出的阵列架构在不知道其自身错误的情况下，将为基于射频指纹的认证和物理层安全提供双重支持。机器学习方法，特别是深度神经网络，将用于使用增强的射频指纹进行认证，与使用常规天线阵列的射频指纹相比，这将产生更高的准确性。为了从数学上分析认证能力的提高，将研究认证成功概率和攻击成功概率的信息论界。还将研究可能的对抗性机器学习攻击混沌天线阵列和防御策略。最后，从无线通信的角度来看，可实现的数据传输速率和安全性的改善，对窃听者将分别考虑增强无线信道多样性和更具挑战性的信道估计条件窃听者进行分析，这反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.