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Related Experiment Videos

Enhancing virtual physically unclonable function security through neuron-criticality analysis and lightweight

Raviha Khan1, Hani Saleh2, Brahim Mefgouda3

  • 1Center for Cyber-Physical Systems - System on Chip Lab and Computer and Information Engineering Department, Khalifa University, Abu Dhabi, UAE.

Scientific Reports
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances virtual physically unclonable functions (VPUFs) security using a novel encryption framework. It achieves high added security with minimal latency, protecting IoT devices from reverse engineering.

Keywords:
IoT securityNeural network encryptionNeuron ablationPUFRayleigh fadingVPUFXOR encryption

Related Experiment Videos

Area of Science:

  • Computer Science
  • Cybersecurity
  • Hardware Security

Background:

  • Physically Unclonable Functions (PUFs) offer hardware-based device authentication using manufacturing variability.
  • Traditional PUFs face limitations like hardware overhead, aging, and vulnerability to attacks.
  • Virtual PUFs (VPUFs) were introduced as a software-based alternative using neural networks for IoT environments.

Purpose of the Study:

  • To enhance the security of Virtual Physically Unclonable Functions (VPUFs) against physical extraction and reverse engineering.
  • To introduce a lightweight, neuron-criticality-aware encryption framework for VPUFs.
  • To maintain VPUF performance and accuracy while improving security.

Main Methods:

  • Developed a lightweight encryption framework focusing on critical neurons identified through ablation analysis.
  • Applied selective XOR-based encryption to minimize computational overhead.
  • Integrated a dynamic key generation mechanism using Rayleigh fading (Jake's model).

Main Results:

  • Achieved up to 99.4% added security against VPUF model extraction and reverse engineering.
  • Demonstrated microsecond-scale latency, preserving authentication accuracy.
  • Successfully identified and utilized critical neurons for efficient encryption.

Conclusions:

  • The proposed neuron-criticality-aware encryption framework significantly enhances VPUF security.
  • This approach offers a scalable and efficient solution for securing IoT devices.
  • The method balances robust security with minimal performance impact.