A Lightweight and Machine-Learning-Resistant PUF framework based on Nonlinear Structure and Obfuscating Challenges-Reference-Cited by-同舟云学术

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A Lightweight and Machine-Learning-Resistant PUF framework based on Nonlinear Structure and Obfuscating Challenges

Published:2023-12-13 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)
language:
Short-container-title:

Author:

Ni Tianming¹,Li Fei¹,Peng Qingsong²,Wang Senling³,Wen Xiaoqing⁴

Affiliation:

1. Anhui Polytechnic University,Anhui Engineering Research Center of Vehicle Display Integrated Systems,School of Integrated Circuits,Wuhu 241000,China

2. Hefei University of Technology,School of Microelectronics,China

3. Ehime University,Graduate School of Science and Engineering

4. Kyushu Institute of Technology,Graduate School of Computer Science and Systems Engineering,Japan

Funder

National Natural Science Foundation of China

Anhui Provincial Department of Education

Anhui Polytechnic University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10409268/10409304/10409383.pdf?arnumber=10409383

Reference24 articles.

1. Extended abstract: The butterfly PUF protecting IP on every FPGA

2. Improved Reliability of FPGA-Based PUF Identification Generator Design

3. Methods for Estimating the Convergence of Inter-Chip Min-Entropy of SRAM PUFs

4. PUF Modeling Attacks on Simulated and Silicon Data

5. Effect of Loop Positions on Reliability and Attack Resistance of Feed-Forward PUFs

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