PUF Phenotype CNN-based Authentication Experimental Code and ML Models

<p dir="ltr">The following code and ML models will allow you to recreate/modify the experimental work undertaken in Chapter 3 of the PhD thesis ' Leveraging DRAM-based Physically Unclonable Functions for Enhancing Authentication in Resource-Constrained Applications' by Owen Millwood (and the key publication [1]).</p><p dir="ltr">It includes the trained VGG16 (with and without classification layer) models and trained classical ML classifiers from the work directly.</p><p dir="ltr">Also included is the code which can be modified or executed however you like.</p><p dir="ltr">The dataset compatible with this code can be found using the following DOI: 10.15131/shef.data.26977528</p><p dir="ltr">[1] O. Millwood, J. Miskelly, B. Yang, P. Gope, E. B. Kavun and C. Lin, "PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Group-Based Authentication With DRAM-PUFs Using Machine Learning," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2451-2465, 2023, doi: 10.1109/TIFS.2023.3266624.</p>