Semi-Honest Verifier Generic PUF Obfuscation Scheme HDL Designs and ML_MA Experimental Code

Includes the Delay PUF datasets, Hardware description language (HDL) code and machine learning code used for the experiments which will allow you to recreate/modify the experimental work undertaken in Chapter 5 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]).

The HDL code will enable synthesis of the hardware design of the PUF Obfuscation scheme used in the work. The ML_MA code will allow you to recreate the attacks used for demonstration in PyPuf.

The dataset compatible with this code can be found using the following DOI: 10.15131/shef.data.26977237

[1] O. Millwood et al., "A Privacy-Preserving Protocol Level Approach to Prevent Machine Learning Modelling Attacks on PUFs in the Presence of Semi-Honest Verifiers," 2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), San Jose, CA, USA, 2023, pp. 326-336, doi: 10.1109/HOST55118.2023.10133804.