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 Eric C. Larson Duncan L. MacFarlane Mitchell A. Thornton
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.