Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cause noise, manifesting themselves as errors which are apparent in the initial PUF measurements. This necessitates expensive active error correction techniques which can run counter to the goal of lightweight security. ML-based techniques for authenticating noisy PUF measurements were explored as an alternative to error correction techniques, bringing about the concept of a PUF Phenotype, where PUF identity is considered as a structure agnostic representation of the PUF, with relevant noise encoding. This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication. Upon conducting security and performance analyses, it is evident that our proposed scheme demonstrates resilience against various attacks compared to the currently existing PUF protocols.

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