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Mohammad Javad Adel1, Mohammad Hadi Rezayati1, Mohammad Hossein Moaiyeri2

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Summary

This study introduces a novel hardware security circuit using magnetoresistive random access memory (MRAM) for physical unclonable functions (PUFs). The MRAM-based PUF demonstrates strong resistance against machine learning attacks, ensuring secure electronic device information.

Keywords:
Deep learning (DL)-based modeling attackEmerging technologiesHardware security primitivesMachine learning (ML)-based modeling attackMagnetic tunnel junction (MTJ)Physical unclonable function (PUF)

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Materials Science

Background:

  • Electronic devices require advanced hardware security to protect sensitive data.
  • Physical Unclonable Functions (PUFs) offer a promising solution for hardware security by leveraging unique device variations.
  • Existing PUF designs face challenges in meeting stringent security criteria and resisting sophisticated attacks.

Purpose of the Study:

  • To propose and evaluate a novel Physical Unclonable Function (PUF) circuit based on Magnetoresistive Random Access Memory (MRAM).
  • To assess the security and performance of the MRAM-based PUF against various machine learning (ML) and deep learning (DL) modeling attacks.
  • To analyze the circuit's efficiency at the layout level and its adherence to security standards like the Strict Avalanche Criterion (SAC).

Main Methods:

  • Designed a PUF circuit utilizing the inherent fabrication variations of Magnetic Tunnel Junction (MTJ) cells in MRAM.
  • Simulated machine learning attacks, including Multilayer Perceptron (MLP), Linear Regression (LR), and Support Vector Machine (SVM), on two-array and four-array architectures.
  • Employed deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for high-dimensional attack simulations.
  • Evaluated intra- and inter-Hamming distance (HD) and diffuseness for the circuit's performance analysis.

Main Results:

  • The MRAM-based PUF effectively satisfies the Strict Avalanche Criterion (SAC), outperforming traditional Arbiter PUFs.
  • Machine learning attacks showed low prediction accuracy (e.g., MLP at 53.61% for two-array, 49.87% for four-array), indicating robustness.
  • Deep learning attacks also yielded low accuracies (around 50.31%), confirming resistance to advanced modeling.
  • The circuit exhibited favorable intra-Hamming distance (0.98%) and diffuseness (49.09%), with acceptable inter-Hamming distance (49.96%).

Conclusions:

  • The proposed MRAM-based PUF circuit offers a robust and secure hardware security solution for electronic devices.
  • Its grid-like structure and reliance on MTJ resistance variations provide strong defense against ML/DL modeling attacks.
  • The circuit meets key performance metrics, making it a viable option for next-generation hardware security applications.