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As the demand for highly secure and dependable lightweight systems increasesin the modern world, Physically Unclonable Functions (PUFs) continue to promisea lightweight alternative to high-cost encryption techniques and secure keystorage. While the security features promised by PUFs are highly attractive forsecure system designers, they have been shown to be vulnerable to varioussophisticated attacks - most notably Machine Learning (ML) based modellingattacks (ML-MA) which attempt to digitally clone the PUF behaviour and thusundermine their security. More recent ML-MA have even exploited publicly knownhelper data required for PUF error correction in order to predict PUF responseswithout requiring knowledge of response data. In response to this, research isbeginning to emerge regarding the authentication of PUF devices with theassistance of ML as opposed to traditional PUF techniques of storage andcomparison of pre-known Challenge-Response pairs (CRPs). In this article, wepropose a classification system using ML based on a novel `PUF-Phenotype'concept to accurately identify the origin and determine the validity of noisymemory derived (DRAM) PUF responses as an alternative to helper data-reliantdenoising techniques. To our best knowledge, we are the first to performclassification over multiple devices per model to enable a group-based PUFauthentication scheme. We achieve up to 98\% classification accuracy using amodified deep convolutional neural network (CNN) for feature extraction inconjunction with several well-established classifiers. We also experimentallyverified the performance of our model on a Raspberry Pi device to determine thesuitability of deploying our proposed model in a resource-constrainedenvironment.

PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning