  Physically unclonable functions (PUFs) are designed to act as device
'fingerprints.' Given an input challenge, the PUF circuit should produce an
unpredictable response for use in situations such as root-of-trust applications
and other hardware-level cybersecurity applications. PUFs are typically
subcircuits present within integrated circuits (ICs), and while conventional IC
PUFs are well-understood, several implementations have proven vulnerable to
malicious exploits, including those perpetrated by machine learning (ML)-based
attacks. Such attacks can be difficult to prevent because they are often
designed to work even when relatively few challenge-response pairs are known in
advance. Hence the need for both more resilient PUF designs and analysis of
ML-attack susceptibility. Previous work has developed a PUF for photonic
integrated circuits (PICs). A PIC PUF not only produces unpredictable responses
given manufacturing-introduced tolerances, but is also less prone to
electromagnetic radiation eavesdropping attacks than a purely electronic IC
PUF. In this work, we analyze the resilience of the proposed photonic PUF when
subjected to ML-based attacks. Specifically, we describe a computational PUF
model for producing the large datasets required for training ML attacks; we
analyze the quality of the model; and we discuss the modeled PUF's
susceptibility to ML-based attacks. We find that the modeled PUF generates
distributions that resemble uniform white noise, explaining the exhibited
resilience to neural-network-based attacks designed to exploit latent
relationships between challenges and responses. Preliminary analysis suggests
that the PUF exhibits similar resilience to generative adversarial networks,
and continued development will show whether more-sophisticated ML approaches
better compromise the PUF and -- if so -- how design modifications might
improve resilience.
