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Ising-PUF: A Machine Learning Attack Resistant PUF Featuring Lattice Like Arrangement of Arbiter-PUFs

机译:ising-puf:一种机器学习攻击抵抗PUF,具有像arbiter-puf的布置等格子

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A concept of Ising-PUF, a novel PUF structure that utilizes chaotic behavior of mutually interacting small PUFs, is proposed. Ising-PUF consists of a lattice like arrangement of small PUFs, each of which contains a spin register that stores the response of the small PUF, which also serves as a challenge of its neighbors. The spin patterns that develop along time determine the 1-bit response of the Ising-PUF. Utilizing state-memorizing nature of the spin registers, Ising-PUF attains a challenge hysteresis, i.e., allowing sequence of challenge inputs that continuously stimulate its chaotic behavior, which provides the drastically large challenge-to-response space. Experimental results demonstrate nearly ideal metrics; inter-chip Hamming distance (HD) of 50.1% and inter-environment HD of 2.26%. Further, Ising-PUF is remarkably tolerant to machine learning attacks, demonstrating that, even with a deep neural network using a 50k training CRPs, the prediction accuracy remains 50%, which is comparable to a random guess.
机译:提出了一种利用相互交互小PUF的混沌行为的新型PUF结构的概念。 ising-puf由像小puf的排列一样的晶格组成,每个字体包含一个旋转寄存器,其存储小puf的响应,这也用作邻居的挑战。开发沿时间的旋转模式确定了Ising-Puf的1位响应。利用旋转寄存器的状态记忆性质,ising-puf呈现挑战滞后,即允许挑战率的序列,这些输入连续刺激其混沌行为,这为响应的响应空间提供了众所周知的大挑战空间。实验结果表明了几乎理想的指标;片间汉明距离(HD)为50.1%和环境间高清2.26%。此外,对于机器学习攻击而言,ising-puf非常宽容,表明,即使使用使用50k训练CRP的深神经网络,预测精度仍然是50%,其与随机猜测相当。

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