Machine Learning Resistant Amorphous Silicon Physically Unclonable
  Functions (PUFs)
Velat Kilic Neil Macfarlane Jasper Stround Samuel Metais Milad Alemohammad A. Brinton Cooper Amy C. Foster Mark A. Foster
We investigate usage of nonlinear wave chaotic amorphous silicon (a-Si)
cavities as physically unclonable functions (PUF). Machine learning attacks on
integrated electronic PUFs have been demonstrated to be very effective at
modeling PUF behavior. Such attacks on integrated a-Si photonic PUFs are
investigated through application of algorithms including linear regression,
k-nearest neighbor, decision tree ensembles (random forests and gradient
boosted trees), and deep neural networks (DNNs). We found that DNNs performed
the best among all the algorithms studied but still failed to completely break
the a-Si PUF security which we quantify through a private information metric.
Furthermore, machine learning resistance of a-Si PUFs were found to be directly
related to the strength of their nonlinear response.