Lightweight Modeling Attack-Resistant Multiplexer-Based Multi-PUF (MMPUF) Design on FPGA

Yijun Cui, Chongyan Gu, Qingqing Ma, Yue Fang, Chenghua Wang, Maire O'Neill, Weiqiang Liu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
19 Downloads (Pure)

Abstract

Physical unclonable function (PUF) is a primary hardware security primitive that is suitable for lightweight applications. However, it is found to be vulnerable to modeling attacks using machine learning algorithms. In this paper, multiplexer (MUX)-based Multi-PUF (MMPUF) design is proposed to thwart modeling attacks. The proposed design uses a weak PUF to obfuscate the challenge ofa strong PUF. A mathematical model of the proposed design is presented and analyzed. The threemost widely used modeling attack techniques are used to evaluate the resistance of the proposed design. Experimental results show that the proposed MMPUF design is more resistant to the machine learning attack than the previously proposed XOR-based Multi-PUF (XMPUF) design. For a large sample size, the prediction rate of the proposed MMPUF is less than the conventional Arbiter PUF (APUF). Compared with existing attack-resistant PUF designs, the proposed MMPUF design demonstrates high resistance. To verify the proposed design, a hardware implementation on Xilinx7 Series FPGAs is presented. The hardware experimental results show that the proposed MMPUF designs present good results of uniqueness and reliability.
Original languageEnglish
Article number815
Number of pages21
JournalElectronics
Volume9
DOIs
Publication statusPublished - 15 May 2020

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