Designing a Photonic Physically Unclonable Function Having Resilience to Machine Learning Attacks

• URL: http://arxiv.org/abs/2404.02440v1
• Date: Wed, 3 Apr 2024 03:58:21 GMT
• Title: Designing a Photonic Physically Unclonable Function Having Resilience to Machine Learning Attacks
• Authors: Elena R. Henderson, Jessie M. Henderson, Hiva Shahoei, William V. Oxford, Eric C. Larson, Duncan L. MacFarlane, Mitchell A. Thornton,
• Abstract summary: We describe a computational PUF model for producing datasets required for training machine learning (ML) attacks. We find that the modeled PUF generates distributions that resemble uniform white noise. Preliminary analysis suggests that the PUF exhibits similar resilience to generative adversarial networks.
• Score: 2.369276238599885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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