A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks
<|reference_start|>A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks: 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.<|reference_end|>
@article{henderson2024a,
    title={A Photonic Physically Unclonable Function's Resilience to
  Multiple-Valued Machine Learning Attacks},
    author={Jessie M. Henderson, Elena R. Henderson, Clayton A. Harper, Hiva
  Shahoei, William V. Oxford, Eric C. Larson, Duncan L. MacFarlane, and
  Mitchell A. Thornton},
    journal={arXiv preprint arXiv:2403.01299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01299},
    primaryClass={cs.CR cs.LG}
}