  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.
