论文标题
深入：使用对XOR BR和TBR PUF的简化数学模型的深度学习技术（攻击和对策）
Going Deep: Using deep learning techniques with simplified mathematical models against XOR BR and TBR PUFs (Attacks and Countermeasures)
论文作者
论文摘要
本文通过使用简化的数学模型和深度学习（DL）技术来评估XOR BR PUF，XOR TBR PUF和XOR BR PUF的混淆架构，从而有助于研究PUF的脆弱性，以防止建模攻击。获得的结果表明，DL建模攻击很容易破坏具有建模精度$ \ sim $ 99％的4输入XOR BR PUF和4输入XOR TBR PUF的安全性。使用单层神经网络（NN）执行了类似的攻击，并使用多项式内核进行了支持向量机（SVM），并且获得的结果表明，单个NNS未能打破PUF安全性。此外，SVM结果证实了先前研究中报告的相同建模精度（$ \ sim $ 50％）。这项研究首次经验表明，DL网络可以用作强大的建模技术，以针对这些复杂的PUF体系结构，以前的传统机器学习技术失败了。此外，关于PUF的舞台大小和复杂性，在DL网络上进行了详细的可伸缩性分析。分析表明，每一层内部的层和隐藏神经元的数量与PUF的舞台大小都有线性关系，这与深度学习中的理论发现一致。因此，引入了一种新的混淆体系结构作为反DL建模攻击的第一步，它显示出对这种攻击的显着抵抗力（精度降低了16％-40％）。这项研究为优先考虑引入新的PUF架构的努力提供了重要的一步，这些PUF架构对建模攻击更加安全和无敌。此外，它触发了关于去除有影响力的位的未来讨论，以及确认特定的PUF体系结构所需的混淆水平具有抵抗强大的DL建模攻击。
This paper contributes to the study of PUFs vulnerability against modeling attacks by evaluating the security of XOR BR PUFs, XOR TBR PUFs, and obfuscated architectures of XOR BR PUF using a simplified mathematical model and deep learning (DL) techniques. Obtained results show that DL modeling attacks could easily break the security of 4-input XOR BR PUFs and 4-input XOR TBR PUFs with modeling accuracy $\sim$ 99%. Similar attacks were executed using single-layer neural networks (NN) and support vector machines (SVM) with polynomial kernel and the obtained results showed that single NNs failed to break the PUF security. Furthermore, SVM results confirmed the same modeling accuracy reported in previous research ($\sim$ 50%). For the first time, this research empirically shows that DL networks can be used as powerful modeling techniques against these complex PUF architectures for which previous conventional machine learning techniques had failed. Furthermore, a detailed scalability analysis is conducted on the DL networks with respect to PUFs' stage size and complexity. The analysis shows that the number of layers and hidden neurons inside every layer has a linear relationship with PUFs' stage size, which agrees with the theoretical findings in deep learning. Consequently, A new obfuscated architecture is introduced as a first step to counter DL modeling attacks and it showed significant resistance against such attacks (16% - 40% less accuracy). This research provides an important step towards prioritizing the efforts to introduce new PUF architectures that are more secure and invulnerable to modeling attacks. Moreover, it triggers future discussions on the removal of influential bits and the level of obfuscation needed to confirm that a specific PUF architecture is resistant against powerful DL modeling attacks.
