Physically unclonable functions (PUFs) identify integrated circuits using nonlinearly-related challenge-response pairs (CRPs). Ideally, the relationship between challenges and corresponding responses is unpredictable, even if a subset of CRPs is known. Previous work developed a photonic PUF offering improved security compared to non-optical counterparts. Here, we investigate this PUF's susceptibility to Multiple-Valued-Logic-based machine learning attacks. We find that approximately 1,000 CRPs are necessary to train models that predict response bits better than random chance. Given the significant challenge of acquiring a vast number of CRPs from a photonic PUF, our results demonstrate photonic PUF resilience against such attacks.
@article{arxiv.2403.01299,
title = {A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks},
author = {Jessie M. Henderson and Elena R. Henderson and Clayton A. Harper and Hiva Shahoei and William V. Oxford and Eric C. Larson and Duncan L. MacFarlane and Mitchell A. Thornton},
journal= {arXiv preprint arXiv:2403.01299},
year = {2024}
}