Physical Unclonable Functions (PUFs) are emerging as promising security primitives for IoT devices, providing device fingerprints based on physical characteristics. Despite their strengths, PUFs are vulnerable to machine learning (ML) attacks, including conventional and reliability-based attacks. Conventional ML attacks have been effective in revealing vulnerabilities of many PUFs, and reliability-based ML attacks are more powerful tools that have detected vulnerabilities of some PUFs that are resistant to conventional ML attacks. Since reliability-based ML attacks leverage information of PUFs' unreliability, we were tempted to examine the feasibility of building defense using reliability enhancing techniques, and have discovered that majority voting with reasonably high repeats provides effective defense against existing reliability-based ML attack methods. It is known that majority voting reduces but does not eliminate unreliability, we are motivated to investigate if new attack methods exist that can capture the low unreliability of highly but not-perfectly reliable PUFs, which led to the development of a new reliability representation and the new representation-enabled attack method that has experimentally cracked PUFs enhanced with majority voting of high repetitions.

翻译：物理不可克隆函数（PUFs）作为一种新兴的安全原语，为物联网设备提供了基于物理特征的设备指纹。尽管其优势显著，PUFs仍易受机器学习（ML）攻击，包括传统攻击和基于可靠性的攻击。传统ML攻击已有效揭示了许多PUFs的漏洞，而基于可靠性的ML攻击作为更强大的工具，已检测出一些能抵抗传统ML攻击的PUFs的漏洞。由于基于可靠性的ML攻击利用了PUFs的不可靠性信息，我们尝试探究利用可靠性增强技术构建防御的可行性，并发现通过合理高次重复的多数表决机制能为现有基于可靠性的ML攻击方法提供有效防御。已知多数表决虽能降低但无法完全消除不可靠性，这促使我们探究是否存在能捕捉高度可靠但非完美可靠PUFs的低不可靠性的新型攻击方法。由此，我们开发了一种新的可靠性表征方式，以及基于该表征的新型攻击方法，实验证明该方法能成功破解采用高重复次数多数表决增强的PUFs。