The physical unclonable function (PUF) is regarded as the root of trust of hardware systems. However, it suffers from the modeling attacks based on machine learning (ML) algorithms. Subsequently, the anti-modeling-attack PUF is of great concern from academia and industries in recent years. In practice, the security of a given PUF is evaluated after the pertinent attacks. However, these evaluation methods are not helpful for the PUF design, because the relationship between the PUF structure and the anti-modeling-attack capability is not established explicitly. This work proposes a security evaluation method of PUF based on the mimic attack and the Probably Approximately Correct (PAC) theory. The anti-modeling-attack capability of PUF is measured by the corresponding area enclosed by the evaluation curve. Twenty representative types of PUFs with different sizes are evaluated by the proposed method. It shows that the proposed method is effective, because the evaluation results are consistent with the difficulties of modeling attacks for the corresponding PUFs in the design practices. And the evaluation results are able to assist the PUF design.

中文翻译：

---

PUF抗建模攻击能力的评估方法


物理不可克隆功能（PUF）被视为硬件系统的信任根。然而，它遭受了基于机器学习（ML）算法的建模攻击。随后，抗建模攻击的PUF近年来受到学术界和工业界的高度关注。实际上，给定 PUF 的安全性是在相关攻击发生后进行评估的。然而，这些评估方法对PUF设计没有帮助，因为PUF结构和抗建模攻击能力之间的关系没有明确建立。本文提出了一种基于模仿攻击和大概正确（PAC）理论的PUF安全评估方法。 PUF的抗建模攻击能力是通过评估曲线所包围的相应面积来衡量的。通过所提出的方法评估了 20 种具有不同尺寸的代表性 PUF。结果表明，该方法是有效的，评估结果与设计实践中相应PUF的建模攻击难度一致。评估结果能够辅助PUF的设计。