Abstract

<p indent="0mm">To solve the problem that the physical unclonable function (PUF) structure is simple and vulnerable to modeling attacks, a strong PUF anti-attack obfuscation design based on linear feedback shift register (LFSR) is proposed. First, a fixed structure LFSR is used as a pseudo-random number generator to provide a random selection signal for the obfuscation logic. Then, a dynamic LFSR with multiple feedback polynomials is used as the obfuscation logic to obfuscate origin challenges. Finally, obfuscated challenges are loaded into the embedded PUF circuit so that the attacker cannot obtain real challenges. It improves the resistance of the PUF to modeling attacks. The proposed design is simulated by Python and FPGA. Experiments on the collected dataset show that the proposed PUF has ideal uniformity (49.8%) and uniqueness (49.9%) and keeps the same reliability. It has simple architecture and low hardware overhead and can resist a variety of modeling attacks including machine learning and deep learning.

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