A Modeling Attack Resilient Strong PUF with Feedback-SPN Structure Having <0.73% Bit Error Rate Through In-Cell Hot-Carrier Injection Burn-In

📋 论文概要

本文提出一种基于反馈SPN（Substitution-Permutation Network）结构的新型强PUF，利用单元内热载流子效应实现低误码率。该设计显著增强了非线性，有效抵抗建模攻击，并实现了小于0.73%的比特错误率，适用于低功耗、低延迟的物联网认证场景。

💡 主要创新点

• 提出反馈SPN结构以增加PUF响应的非线性，从而提升对建模攻击的抵抗能力。
• 利用单元内热载流子效应实现高可靠性，使得误码率低于0.73%，同时保持低功耗和低延迟。

🏷 关键词

📄 原文摘要

lowenergy and low-latency authentication requirements of IoT applications, owing to their exponential number of challenge-response pairs (CRPs). However, Strong PUFs suffer from vulnerability to modeling attacks and a high bit-error rate (BER). The first Strong PUF, known as the arbiter PUF, has little tolerance against modeling attacks because of the linear summation of path-delay times in its response [1]. Several studies have been conducted to improve immunity by introducing non-linearity in the response-generation procedure [2-6]. Out of these, only look-up-table (LUT)-based solutions [2,6] achieved a high machine-learning (ML) robustness against more than 0.1M training CRPs. However, the design in [2] requires 112K bits of entropy, and that in [6] uses many AES

👥 作者与机构

Kunyang Liu, Zihan Fu, Gen Li, Hongliang Pu, Zhibo Guan, Xingyu Wang,

Xinpeng Chen, Hirofumi Shinohara Waseda University, Kitakyushu, Japan Strong physically unclonable functions (Strong PUFs) are expected to meet the