面向抗学习型攻击的具备弹性与耐久性感知的基于NVM的PUF
Resilient Endurance-Aware NVM-based PUF against Learning-based Attacks
AI总结：
针对基于NVM的PUF存在的耐久性不足问题，本文提出磨损影响预测模型，并设计出写入均衡的新型PUF，实现耐久性62倍提升且保留抗学习攻击能力。
摘要：
基于非易失性存储器（Non-Volatile Memory，NVM）技术的物理不可克隆函数（Physical Unclonable Functions，PUFs）已成为安全认证和密码应用领域的一种极具前景的解决方案。利用NVM的多级单元（multi-level cell，MLC）特性，这类PUF可生成大量独特响应，增强了其对机器学习（machine learning，ML）建模攻击的抵御能力。然而，基于NVM的PUF存在一个显著问题：耐久性不足；频繁的写入操作会随时间推移导致单元磨损和性能退化，降低PUF的可靠性和使用寿命。\n 本文针对这些问题，提出了一个综合模型，用于预测和分析耐久性变化对NVM PUF的影响。该模型揭示了磨损如何影响PUF的质量，有助于设计更稳健的PUF。基于此模型，我们提出了一种新型NVM PUF设计，可显著提升耐久性。我们的设计方法采用先进技术，使写入操作分布更均匀，降低单个单元的压力。实验结果表明，与当前最先进的方案相比，该NVM PUF的耐久性提升了62倍，同时保持了对学习型攻击的防护能力。
英文摘要：
Physical Unclonable Functions (PUFs) based on Non-Volatile Memory (NVM) technology have emerged as a promising solution for secure authentication and cryptographic applications. By leveraging the multi-level cell (MLC) characteristic of NVMs, these PUFs can generate a wide range of unique responses, enhancing their resilience to machine learning (ML) modeling attacks. However, a significant issue with NVM-based PUFs is their endurance problem; frequent write operations lead to wear and degradation over time, reducing the reliability and lifespan of the PUF. This paper addresses these issues by offering a comprehensive model to predict and analyze the effects of endurance changes on NVM PUFs. This model provides insights into how wear impacts the PUF's quality and helps in designing more robust PUFs. Building on this model, we present a novel design for NVM PUFs that significantly improves endurance. Our design approach incorporates advanced techniques to distribute write operations more evenly and reduce stress on individual cells. The result is an NVM PUF that demonstrates a $62\times$ improvement in endurance compared to current state-of-the-art solutions while maintaining protection against learning-based attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
