A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks

• Published in: 2024 IEEE 54th International Symposium on Multiple-Valued Logic (ISMVL) pp. 161 - 166
• Main Authors: Henderson, Jessie M., Henderson, Elena R., Harper, Clayton A., Shahoei, Hiva, Oxford, William V., Larson, Eric C., MacFarlane, Duncan L., Thornton, Mitchell A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28-05-2024
• Subjects: cybersecurity; Integrated circuits; Logic; Machine learning; multiple-valued logic; neural network; Photonics; Physical unclonable function; physically unclonable function; Predictive models; Security; susceptibility
• ISSN: 2378-2226
• DOI: 10.1109/ISMVL60454.2024.00039

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.