A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917602795716608 |
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| author | Henderson, Jessie M. Henderson, Elena R. Harper, Clayton A. Shahoei, Hiva Oxford, William V. Larson, Eric C. MacFarlane, Duncan L. Thornton, Mitchell A. |
| author_facet | Henderson, Jessie M. Henderson, Elena R. Harper, Clayton A. Shahoei, Hiva Oxford, William V. Larson, Eric C. MacFarlane, Duncan L. Thornton, Mitchell A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks Henderson, Jessie M. Henderson, Elena R. Harper, Clayton A. Shahoei, Hiva Oxford, William V. Larson, Eric C. MacFarlane, Duncan L. Thornton, Mitchell A. Cryptography and Security Machine Learning 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. |
| title | A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2403.01299 |