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

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.