A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks

Jessie M. Henderson, Elena R. Henderson, Clayton A. Harper, Hiva Shahoei, William V. Oxford +3 more
3/2/2024
cs.CRcs.LG

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Abstract

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.

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Cite this paper

@article{henderson2024photonic,
  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},
  year   = {2024},
  eprint = {2403.01299},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.01299v1}
}

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