An arbiter physical unclonable function (APUF) has exponential challenge‐response pairs and is easy to implement on field‐programmable gate arrays (FPGAs). However, modeling attacks based on machine learning have become a serious threat to APUFs. Although the modeling‐attack resistance of an MA‐APUF has been improved considerably by architecture modifications, the response generation method of an MA‐APUF results in low uniqueness. In this study, we demonstrate three design problems regarding the low uniqueness that APUF‐based strong PUFs may exhibit, and we present several foundational principles to improve the uniqueness of APUF‐based strong PUFs. In particular, an improved MA‐APUF design is implemented in an FPGA and evaluated using a well‐established experimental setup. Two types of evaluation metrics are used for evaluation and comparison. Furthermore, evolution strategies, logistic regression, and K‐junta functions are used to evaluate the security of our design. The experiment results reveal that the uniqueness of our improved MA‐APUF is 81.29% (compared with that of the MA‐APUF, 13.12%), and the prediction rate is approximately 56% (compared with that of the MA‐APUF (60%‐80%).

中文翻译：

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具有更高的唯一性和安全性的改进的MA-APUF的设计和实现

仲裁器物理不可克隆功能（APUF）具有指数的质询-响应对，并且易于在现场可编程门阵列（FPGA）上实现。但是，基于机器学习的建模攻击已成为对APUF的严重威胁。尽管通过体系结构修改已大大改善了MA-APUF的建模攻击抵抗力，但MA-APUF的响应生成方法却导致其唯一性较低。在本研究中，我们演示了三个有关基于APUF的强PUF可能表现出的低唯一性的设计问题，并且我们提出了一些基本原理来提高基于APUF的强PUF的唯一性。特别是，改进的MA-APUF设计在FPGA中实现，并使用完善的实验设置进行评估。两种类型的评估指标用于评估和比较。K junta函数用于评估我们设计的安全性。实验结果表明，改进后的MA-APUF的唯一性为81.29％（相比之下，MA-APUF为13.12％），预测率约为56％（与MA-APUF相比，预测率为60％ ‐80％）。