Physical unclonable functions (PUF) are emerging as a promising alternative to traditional cryptographic protocols for IoT authentication. XOR Arbiter PUFs (XPUFs), a group of well-studied PUFs, are found to be secure against machine learning (ML) attacks if the XOR gate is large enough, as both the number of CRPs and the computational time required for modeling n-XPUF increases fast with respect to n, the number of component arbiter PUFs. In this paper, we present a neural network-based method that can successfully attack XPUFs with significantly fewer CRPs and shorter learning time when compared with existing ML attack methods. Specifically, the experimental study in this paper shows that our new method can break the 64-bit 9-XPUF within ten minutes of learning time for all of the tested samples and runs, with magnitudes faster than the fastest existing ML attack method, which takes over 1.5 days of parallel computing time on 16 cores.

中文翻译：

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XOR PUF安全漏洞研究的快速深度学习方法

物理不可克隆功能（PUF）逐渐成为有希望的替代物联网身份验证的传统加密协议的替代方案。XOR仲裁者的PUF（XPUFs），一组充分研究的PUF的，被发现对机器学习安全（ML）的攻击，如果异或门是足够大的，如协调研究项目的数量和建模所需的计算时间ñ - XPUF相对于n迅速增加，组件仲裁器PUF的数量。在本文中，我们提出了一种基于神经网络的方法，与现有的ML攻击方法相比，该方法可以用更少的CRP和较短的学习时间成功攻击XPUF。具体而言，本文的实验研究表明，我们的新方法可以在10分钟的学习时间内打破所有测试样本和运行的64位9-XPUF，其幅度要比现有最快的ML攻击方法快得多。在16个内核上超过1.5天的并行计算时间。