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Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable

Subin Lee1, Byung Chul Jang2,3, Minseo Kim1

  • 1Department of Electronic Engineering Gachon University, 1342 Seongnam-daero, Seongnam, 13120, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 17, 2023
PubMed
Summary

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This summary is machine-generated.

This study developed a novel graphene-based security key using mixed silane layers to resist machine learning (ML) attacks. The new key demonstrates superior security performance and a significantly lower ML attack accuracy compared to existing methods.

Area of Science:

  • Materials Science
  • Nanotechnology
  • Cybersecurity

Background:

  • Graphene's unique electronic properties offer potential for advanced security applications.
  • Existing graphene-based security keys are vulnerable to sophisticated machine learning (ML) attacks.
  • Developing robust, unforgeable security keys is crucial for data protection.

Purpose of the Study:

  • To engineer a graphene-based security key with enhanced resistance to ML attacks.
  • To investigate the use of mixed octadecyltrichlorosilane (ODTS) and perfluorooctyltriethoxysilane (FOTS) layers for disordered doping.
  • To evaluate the security performance and uniqueness of the generated security keys.

Main Methods:

  • Fabrication of graphene devices with mixed ODTS and FOTS layers.
Keywords:
graphenemachine learning attackphysical unclonable functionraman spectroscopyself-assembled monolayer

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  • Generation of a 12x12 array of current-based security keys at 100 mV.
  • Characterization using Raman spectroscopy to confirm uniqueness and carrier concentration.
  • Assessment of security key uniformity and inter-Hamming distance (HD).
  • Evaluation of ML attack accuracy against the developed security system.

    • Main Results:

    • The mixed silane layers induced a disordered doping state in graphene.
    • The security keys exhibited high uniformity (50.0 ± 12.3%) and inter-Hamming distance (45.5 ± 16.7%).
    • Raman spectroscopy confirmed the uniqueness of 10,000 data points by analyzing G peak shifts.
    • The developed defense system achieved a low ML attack accuracy of 10.33%.
    • A FOTS-only graphene device showed significantly higher ML attack accuracy (44.81%).

    Conclusions:

    • Mixed ODTS/FOTS layers on graphene create a robust security key resistant to ML attacks.
    • The disordered doping state and unique key generation provide superior security compared to previous graphene-based keys.
    • This approach offers a promising pathway for developing next-generation secure hardware.