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Design and implementation of an improved MA-APUF with higher uniqueness and security

  • Li, Bing (School of Microelectronics, School of Cyber Science and Engineering, SEU-FiberHome Joint Research Center, Southeast University) ;
  • Chen, Shuai (School of Microelectronics, School of Cyber Science and Engineering, SEU-FiberHome Joint Research Center, Southeast University) ;
  • Dan, Fukui (School of Microelectronics, School of Cyber Science and Engineering, SEU-FiberHome Joint Research Center, Southeast University)
  • Received : 2019.02.20
  • Accepted : 2019.08.28
  • Published : 2020.04.03

Abstract

An arbiter physical unclonable function (APUF) has exponential challenge-response pairs and is easy to implement on field-programmable gate arrays (FPGAs). However, modeling attacks based on machine learning have become a serious threat to APUFs. Although the modeling-attack resistance of an MA-APUF has been improved considerably by architecture modifications, the response generation method of an MA-APUF results in low uniqueness. In this study, we demonstrate three design problems regarding the low uniqueness that APUF-based strong PUFs may exhibit, and we present several foundational principles to improve the uniqueness of APUF-based strong PUFs. In particular, an improved MA-APUF design is implemented in an FPGA and evaluated using a well-established experimental setup. Two types of evaluation metrics are used for evaluation and comparison. Furthermore, evolution strategies, logistic regression, and K-junta functions are used to evaluate the security of our design. The experiment results reveal that the uniqueness of our improved MA-APUF is 81.29% (compared with that of the MA-APUF, 13.12%), and the prediction rate is approximately 56% (compared with that of the MA-APUF (60%-80%).

Acknowledgement

Supported by : Shenzhen Science, Technology and Innovation Commission (SZSTI), National Natural Science Foundation of China (NSFC), Southeast University

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