Classical cryptographic methods that inherently employ secret keys embedded in non-volatile memory have been known to be impractical for limited-resource Internet of Things (IoT) devices. Physical Unclonable Functions (PUFs) have emerged as an applicable solution to provide a keyless means for secure authentication. PUFs utilize inevitable variations of integrated circuits (ICs) components, manifest during the fabrication process, to extract unique responses. Double Arbiter PUFs (DAPUFs) have been recently proposed to overcome security issues in XOR PUF and enhance the tolerance of delay-based PUFs against modeling attacks. This paper provides comprehensive risk analysis and performance evaluation of all proposed DAPUF designs and compares them with their counterparts from XOR PUF. We generated different sets of real challenge–response pairs CRPs from three FPGA hardware boards to evaluate the performance of both DAPUF and XOR PUF designs using special-purpose evaluation metrics. We show that none of the proposed designs of DAPUF is strictly preferred over XOR PUF designs. In addition, our security analysis using neural network reveals the vulnerability of all DAPUF designs against machine learning attacks.

中文翻译：

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基于硅数据的双仲裁器PUF的鲁棒性和不可预测性：性能评估和建模精度

固有地采用嵌入在非易失性存储器中的秘密密钥的经典加密方法对于有限资源的物联网（IoT）设备是不切实际的。物理不可克隆功能（PUF）已成为一种可应用的解决方案，可提供用于安全认证的无密钥方式。PUF利用制造过程中明显出现的集成电路（IC）组件不可避免的变化来提取独特的响应。最近提出了双仲裁器PUF（DAPUF），以克服XOR PUF中的安全性问题，并增强基于延迟的PUF对建模攻击的容忍度。本文提供了所有建议的DAPUF设计的全面风险分析和性能评估，并将它们与XOR PUF的同类产品进行了比较。我们从三个FPGA硬件板上生成了不同的真实质询-响应对CRP集，以使用专用评估指标评估DAPUF和XOR PUF设计的性能。我们显示DAPUF的所有拟议设计都不比XOR PUF设计更严格。此外，我们使用神经网络进行的安全性分析揭示了所有DAPUF设计针对机器学习攻击的脆弱性。