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（PUF）正成为物联网设备中极具前景的安全原语，其通过物理特征提供设备指纹。尽管具备显著优势，PUF仍面临机器学习攻击（包括传统攻击与基于可靠性的攻击）的威胁。传统机器学习攻击已有效揭示众多PUF的脆弱性，而基于可靠性的机器学习攻击作为更强大的工具，已检测到部分对传统攻击具有抵抗性的PUF的漏洞。由于基于可靠性的攻击利用了PUF不可靠性的信息，我们尝试探究采用可靠性增强技术构建防御方案的可行性，并发现采用较高重复次数的多数投票机制可有效抵御现有基于可靠性的机器学习攻击方法。已知多数投票能降低但无法完全消除不可靠性，这促使我们研究是否存在能够捕获高可靠性（非绝对可靠）PUF中低不可靠性的新型攻击方法，进而开发出新的可靠性表征方法及基于该表征的攻击技术。实验表明，该方法成功破解了经高重复次数多数投票增强的PUF。