Mathematical Model of Strong Physically Unclonable Functions Based on Hybrid Boolean Networks

July 20, 2022 · Entered Twilight · 🏛 IEEE International Symposium on Hardware Oriented Security and Trust

Repo contents: .devcontainer.json, .github, .gitignore, 00_graph_functions.ipynb, 01_model_functions.ipynb, 02_network_class.ipynb, 03_hbn_puf.ipynb, CONTRIBUTING.md, LICENSE, MANIFEST.in, Makefile, README.md, docker-compose.yml, docs, example.html, index.ipynb, networkm, paper, settings.ini, setup.py

Authors Noeloikeau Charlot, Daniel J. Gauthier, Daniel Canaday, Andrew Pomerance arXiv ID 2207.10816 Category cs.CR: Cryptography & Security Citations 0 Venue IEEE International Symposium on Hardware Oriented Security and Trust Repository https://github.com/Noeloikeau/networkm ⭐ 2 Last Checked 3 months ago

Abstract

We introduce a mathematical framework for simulating Hybrid Boolean Network (HBN) Physically Unclonable Functions (PUFs, HBN-PUFs). We verify that the model is able to reproduce the experimentally observed PUF statistics for uniqueness $μ_{inter}$ and reliability $μ_{intra}$ obtained from experiments of HBN-PUFs on Cyclone V FPGAs. Our results suggest that the HBN-PUF is a true `strong' PUF in the sense that its security properties depend exponentially on both the manufacturing variation and the challenge-response space. Our Python simulation methods are open-source and available at https://github.com/Noeloikeau/networkm.