  As the demand for highly secure and dependable lightweight systems increases
in the modern world, Physically Unclonable Functions (PUFs) continue to promise
a lightweight alternative to high-cost encryption techniques and secure key
storage. While the security features promised by PUFs are highly attractive for
secure system designers, they have been shown to be vulnerable to various
sophisticated attacks - most notably Machine Learning (ML) based modelling
attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus
undermine their security. More recent ML-MA have even exploited publicly known
helper data required for PUF error correction in order to predict PUF responses
without requiring knowledge of response data. In response to this, research is
beginning to emerge regarding the authentication of PUF devices with the
assistance of ML as opposed to traditional PUF techniques of storage and
comparison of pre-known Challenge-Response pairs (CRPs). In this article, we
propose a classification system using ML based on a novel `PUF-Phenotype'
concept to accurately identify the origin and determine the validity of noisy
memory derived (DRAM) PUF responses as an alternative to helper data-reliant
denoising techniques. To our best knowledge, we are the first to perform
classification over multiple devices per model to enable a group-based PUF
authentication scheme. We achieve up to 98\% classification accuracy using a
modified deep convolutional neural network (CNN) for feature extraction in
conjunction with several well-established classifiers. We also experimentally
verified the performance of our model on a Raspberry Pi device to determine the
suitability of deploying our proposed model in a resource-constrained
environment.
