Ibrahim Hebatallah M, Skovorodnikov Heorhii, Alkhzaimi Hoda
EMARATSEC, New York Univeristy Abu Dhabi, Saadiyat Island, Abu Dhabi, 129188, United Arab Emirates.
Sci Rep. 2024 Oct 14;14(1):23962. doi: 10.1038/s41598-024-73839-1.
Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures or secret keys amidst other critical cryptographic applications. CMOS-based PUFs are the most popular type, they generate unique bit strings using process variations in semiconductor fabrication. However, most existing CMOS PUFs are found to be vulnerable to modeling attacks based on machine learning (ML) algorithms. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology, introducing cryptographic and resilient random outputs. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device and the fabrication process level such as the cross-sectional area and variations. Our study focuses on building a machine learning analysis and attack framework of tools on memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using machine learning analysis tools, on-top of holistic NIST cryptographic randomness testing initially provided, to provide a high level of certainty in predicting the randomness output of the verified Memrister-based PUF. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means , Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within and respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF.
物理不可克隆函数（PUF）已成为一种理想的硬件安全原语，在其他关键的加密应用中，它们利用工艺变化来提供唯一签名或密钥。基于CMOS的PUF是最流行的类型，它们利用半导体制造中的工艺变化来生成唯一的比特串。然而，现有的大多数CMOS PUF被发现容易受到基于机器学习（ML）算法的建模攻击。利用纳米技术制造工艺和高度非线性行为的忆阻器成为现有基于CMOS的PUF技术的一个有趣替代方案，可产生加密且具有弹性的随机输出。基于忆阻器的PUF正在兴起，这是由于在忆阻器层面因器件的逐周期（C2C）编程变化以及诸如横截面积和变化等制造工艺层面所固有的随机性。我们的研究重点是构建基于忆阻器的PUF（MR-PUF）的机器学习分析和攻击工具框架。我们的目标是在最初提供的整体NIST加密随机性测试之上，使用机器学习分析工具来测试所提出的PUF安全裕度的弹性，以便在预测经过验证的基于忆阻器的PUF的随机输出时提供高度确定性。我们的主要贡献是一项全面研究，该研究专注于在有效时间和数据复杂度内，基于使用逻辑回归（LR）、支持向量机（SVM）、高斯混合模型（GMM）、K均值、随机森林、XGBoost和长短期记忆网络（LSTM）构建随机预测器来攻击随机输出弹性。我们的结果分别产生了低准确率和ROC结果，分别在[具体数值1]和[具体数值2]以内，表明预测随机数据失败，证明了MR-PUF有效的随机预测弹性。本研究中说明的这些攻击的有效时间和数据复杂度被证明是线性和二次的，导致攻击执行时间以秒为单位，以及5032个训练样本与2157个测试样本相结合来验证PUF的随机性。
