具有265个挑战响应对的强亚阈值电流阵列PUF,可抵御130nm CMOS中的机器学习攻击

Xiaodan Xi, Haoyu Zhuang, Nan Sun, M. Orshansky
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引用次数: 52

摘要

提出了一种免疫机器学习攻击的强硅物理不可克隆函数(PUF)。该PUF称为亚阈值电流阵列(SCA) PUF,由一对二维晶体管阵列和一个低偏置比较器组成。制造的PUF芯片允许265个挑战响应对(CRPs),在- 20至80°C和Vdd + 10%的温度下实现高可靠性,平均误码率(BER)为5.8%。基于校准的CRPs滤波方法有效地将误码率提高到2.6%,CRPs损失为10%。当遭受ML攻击时,PUF显示出比已知替代方案高100倍的弹性,而PUF不可预测性的损失可以忽略不计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strong subthreshold current array PUF with 265 challenge-response pairs resilient to machine learning attacks in 130nm CMOS
This paper presents a strong silicon physically unclonable function (PUF) immune to machine learning (ML) attacks. The PUF, termed the subthreshold current array (SCA) PUF, is composed of a pair of two-dimensional transistor arrays and a low-offset comparator. The fabricated PUF chip allows 265 challenge-response pairs (CRPs) and achieves high reliability with average bit error rate (BER) of 5.8% for temperatures −20 to 80°C and Vdd + 10%. The calibration-based CRPs filtering method effectively improves BER to 2.6% with a 10% loss of CRPs. When subjected to ML attacks, the PUF shows resilience that is 100X higher than known alternatives, with negligible loss in PUF unpredictability.
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