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

arXiv:2403.01299v1 Announce Type: cross
Abstract: 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 …

abstract arxiv attacks challenge challenges cs.cr cs.lg function functions identify integrated circuits machine machine learning multiple optical relationship resilience responses security type work