基于序列密码的强PUF抗机器学习攻击方法

物理不可克隆函数(Physical Unclonable Function, PUF)在信息安全领域具有极其重要的应用前景，然而也存在其自身安全受机器学习攻击等方面的不足。该文通过对PUF电路和密码算法的研究，提出一种基于序列密码的强PUF抗机器学习攻击方法。首先，通过构造滚动密钥生成器产生随机密钥，并与输入激励进行混淆；然后，将混淆后的激励通过串并转换电路作用于强PUF，产生输出响应；最后，利用Python软件仿真和FPGA硬件实现，并分析其安全性和统计特性。实验结果表明，当建模所用激励响应对(Challenge Response Pairs, CRPs)高达106组时，基于逻辑回归、人工神经网络和支持向量机的攻击预测率接近50%的理想值。此外，该方法通用性强、硬件开销小，且不影响PUF的随机性、唯一性以及可靠性。

Sequence Cipher Based Machine Learning-Attack Resistance Method for Strong-PUF

Physical Unclonable Function (PUF) has extremely important application prospects to the field of information security, however, there are also shortcomings in its own security from machine learning attacks and other aspects. By studying PUF circuits and cryptographic algorithm, a method based on sequence cipher of strong-PUF is proposed to resist machine learning attacks. Firstly, the random key is generated by constructing a rolling key generator, which is obfuscated with the input challenge; Then the obfuscated challenge is applied to the strong-PUF through a series-parallel conversion circuit to generate the output response; Finally, Python software simulation and FPGA hardware implementation are used to analyze the safety and statistical properties. The experimental results show that the attack prediction rates based on logistic regression, artificial neural network and support vector machine are close to the ideal value of 50% when the CRPs used for modeling are up to 106 groups. In addition, this method has high versatile, low hardware overhead and does not affect the randomness, uniqueness and reliability of PUF.