Grid Multi-Butterfly Memristive Neural Network With Three Memristive Systems: Modeling, Dynamic Analysis, and Application in Police IoT

Hairong Lin, Xiaoheng Deng, Fei Yu, Yichuang Sun

Research output: Contribution to journalArticlepeer-review

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Abstract

Nowadays, the Internet of Things (IoT) technology
has been widely applied in the police security system. However,
with more and more image data that concerns crime scenes being
transmitted through the police IoT, there are some new security
and privacy issues. Therefore, how to design a safe and efficient
secret image sharing solution suitable for police IoT has become a
very urgent task. In this work, a grid multi-butterfly memristive
Hopfield neural network (HNN) with three memristive systems is
constructed and its complex dynamics are deeply analyzed. Among
them, the first memristive system is modeled by emulating a self
connection synapse, the second memristive system is modeled by
coupling two neurons, and the third memristive system is modeled
by describing external electromagnetic radiation. Dynamic analyses
show that the proposed memristive HNN can not only generate two
kinds of 1-directional (1D) multi-butterfly chaotic attractors but
also produce complex grid (2D) multi-butterfly chaotic attractors.
More importantly, by switching the initial states of the second and
third memristive systems, the grid multi-butterfly memristive HNN
exhibits initial-boosted plane coexisting multi-butterfly attractors.
Moreover, the number of butterflies contained in a multi-butterfly
attractor and coexisting attractors can be easily adjusted by changing
memristive parameters. Based on these complex dynamics, an
image security solution is designed to show the application of the
newly constructed grid multi-butterfly memristive HNN to police
IoT security. Security performances indicate the designed scheme
can resist various attacks and has high robustness. Finally, the
test results are further demonstrated through RPI-based hardware
experiments
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Internet of Things Journal
Early online date19 Jun 2024
DOIs
Publication statusE-pub ahead of print - 19 Jun 2024

Keywords

  • Biological neural networks
  • Couplings
  • Encryption
  • Grid multi-butterfly attractor
  • Internet of Things
  • IoT
  • Law enforcement
  • Memristors
  • Security
  • chaos-based application
  • initial-boosted behavior
  • memristive neural network

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