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Related Concept Videos

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Compartmentalization of Human Stem Cell-Derived Neurons within Pre-Assembled Plastic Microfluidic Chips
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Defense Against Chip Cloning Attacks Based on Fractional Hopfield Neural Networks.

Yi-Fei Pu1, Zhang Yi1, Ji-Liu Zhou1

  • 11 College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.

International Journal of Neural Systems
|October 28, 2016
PubMed
Summary

Fractional Hopfield Neural Networks (FHNNs) offer superior defense against chip cloning compared to Physically Unclonable Functions (PUFs). FHNNs provide enhanced security with lower costs and greater stability against environmental factors.

Keywords:
Defense against chip cloning attacksfractancefractional Hopfield neural networksfractional calculusphysically unclonable function

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Area of Science:

  • Hardware security
  • Artificial intelligence
  • Cybersecurity

Background:

  • Physically Unclonable Functions (PUFs) are a leading hardware security technology.
  • PUFs face limitations including cost, temperature sensitivity, electromagnetic interference (EMI), and limited entropy.
  • Novel mechanisms are needed to overcome PUF weaknesses.

Purpose of the Study:

  • To apply Fractional Hopfield Neural Networks (FHNNs) for defense against chip cloning attacks.
  • To demonstrate FHNNs' superiority over PUFs in hardware security applications.
  • To address the limitations of current PUF technologies.

Main Methods:

  • Implementation of arbitrary-order fractors for FHNNs.
  • Analysis of FHNN implementation costs.
  • Development of constant-order FHNN performance under temperature variations.
  • Evaluation of FHNN electrical performance stability under EMI.
  • Study of FHNN entropy levels.
  • Experimental analysis of FHNN fractor bandwidth and anti-cloning capabilities.

Main Results:

  • FHNNs demonstrate significant advantages over PUFs in defending against chip cloning.
  • FHNNs exhibit lower implementation costs compared to PUFs.
  • FHNNs show superior electrical performance stability across varying temperatures and EMI conditions.
  • FHNNs provide a higher amount of entropy than PUFs of similar circuit scale.

Conclusions:

  • Fractional Hopfield Neural Networks present a robust and cost-effective solution for chip cloning attack defense.
  • FHNNs overcome key limitations of PUFs, offering enhanced hardware security.
  • Experimental results validate the superior anti-cloning, anti-EMI, and anti-temperature variation capabilities of FHNNs.