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Dynamic Challenge Cross-Selection Physical Unclonable Function Based on MRAM
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AI
DOI：10.1109/TVLSI.2025.3600042.png)
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Abstract
En 中文
The rapid development of Internet of Things (IoT) devices has triggered massive data transmission. Meanwhile, advances in artificial intelligence (AI) introduce new security vulnerabilities in device interactions. These challenges demand lightweight yet robust security solutions. In this context, physical unclonable functions (PUFs) serve as critical hardware security primitives, enabling reliable authentication for edge devices. Nevertheless, PUF is increasingly susceptible to novel threats, notably machine learning attacks. To address this security vulnerability to attacks, we propose a novel double-layer dynamic challenge cross-selection magnetoresistive random access memory PUF (MPUF). This design leverages the inherent process variation in spin-transfer torque magnetoresistive random access memory (STT-MRAM) as an entropy source. The proposed structure incorporates an obfuscation decode circuit (ODC) that combines XOR gates and shift registers. It dynamically obfuscates interlayer relationships between two PUF arrays to enhance circuit nonlinearity. The simulation results demonstrate uniformity of 50.16%, uniqueness of 49.94%, a worst bit error rate (BER) of 2.34% for-25 degrees C to 125 degrees C and 1.56% for 0.5 similar to 1.1 V. In addition, four common machine learning models are used to attack this PUF, achieving accuracies of 50.49%, 50.49%, 50.48%, and 58.41%, which are close to a random guess. Compared with traditional PUF implementations, this work exhibits higher reliability and enhanced security while maintaining low power consumption of approximately 9.975 fJ/bit.
Keywords:
High security
machine learning attack
obfuscation
physically unclonable function
spin-transfer torque magnetoresistive random access memory (STT-MRAM)
Journal
I
IF:
3.1
Papers:
440
Citations:
7.3K