Divyanshu Divyanshu, Goyal Amit Kumar, Massoud Yehia
Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia.
Sci Rep. 2024 Jun 22;14(1):14393. doi: 10.1038/s41598-024-65176-0.
This study presents a novel method leveraging surface wave-assisted photonic spin Hall effect (PSHE) to construct physical unclonable functions (PUFs). PUFs exploit inherent physical variations to generate unique Challenge-Response pairs, which are critical for hardware security and arise from manufacturing discrepancies, device characteristics, or timing deviations. We explore PSHE generation-based PUF design, expanding existing design possibilities. With recent applications in precise sensing and computing, PSHE offers promising performance metrics for our proposed PUFs, including an inter-Hamming distance of 47.50% , an average proportion of unique responses of 62.5% , and a Pearson correlation coefficient of - 0.198. The PUF token demonstrates robustness to simulated noise. Additionally, we evaluate security using a machine learning-based attack model, employing a multi-layer perceptron (MLP) regression model with a randomized search method. The average accuracy of successful attack prediction is 9.70% for the selected dataset. Our novel PUF token exhibits high non-linearity due to the PSHE effect, resilience to MLP-based attacks, and sensitivity to process variation.
本研究提出了一种利用表面波辅助光子自旋霍尔效应（PSHE）来构建物理不可克隆函数（PUF）的新方法。PUF利用固有的物理变化来生成独特的挑战-响应对，这对于硬件安全至关重要，并且源于制造差异、器件特性或时序偏差。我们探索基于PSHE生成的PUF设计，扩展了现有的设计可能性。随着PSHE在精确传感和计算中的最新应用，它为我们提出的PUF提供了有前景的性能指标，包括汉明间距为47.50%、唯一响应的平均比例为62.5%以及皮尔逊相关系数为-0.198。PUF令牌对模拟噪声具有鲁棒性。此外，我们使用基于机器学习的攻击模型来评估安全性，采用具有随机搜索方法的多层感知器（MLP）回归模型。对于所选数据集，成功攻击预测的平均准确率为9.70%。由于PSHE效应，我们新颖的PUF令牌表现出高度非线性、对基于MLP的攻击具有弹性以及对工艺变化敏感。
