Dual-Memristor Brain-Like Chaotic Neural Network and Its Application in IoMT Data Privacy Protection

LIN Hairong; DUAN Chenxing; DENG Xiaoheng; Geyong Min

doi:10.11999/JEIT241133

Volume 47 Issue 7

Jul. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(7): 2194-2210

LIN Hairong, DUAN Chenxing, DENG Xiaoheng, Geyong Min. Dual-Memristor Brain-Like Chaotic Neural Network and Its Application in IoMT Data Privacy Protection[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2194-2210. doi: 10.11999/JEIT241133

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LIN Hairong, DUAN Chenxing, DENG Xiaoheng, Geyong Min. Dual-Memristor Brain-Like Chaotic Neural Network and Its Application in IoMT Data Privacy Protection[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2194-2210. doi: 10.11999/JEIT241133

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Dual-Memristor Brain-Like Chaotic Neural Network and Its Application in IoMT Data Privacy Protection

doi: 10.11999/JEIT241133 cstr: 32379.14.JEIT241133

1.
School of Electronic Information, Central South University, Changsha 410083, China
2.
the Department of Computer Science, University of Exter, Exeter, EX4 4QF UK

Funds: The National Natural Science Foundation of China (62201204, 62373372), The Natural Science Foundation of Hunan (2023JJ40168, 2023JJ30696)

Received Date: 2024-12-25
Rev Recd Date: 2025-04-11

Available Online: 2025-04-11

Publish Date: 2025-07-22

Abstract

Abstract

Objective In recent years, frequent breaches of medical data have posed significant threats to patient privacy and health security, highlighting the urgent need for effective solutions to protect medical data privacy and security during transmission. This paper proposes a novel data privacy protection method for the Internet of Medical Things (IoMT) based on a dual-memristor-inspired brain-like chaotic neural network to address this challenge. Methods Leveraging the synaptic bionic characteristics of memristors, a dual-memristor brain-like chaotic neural network model based on the Hopfield neural network is developed. The complex chaotic dynamics of this model are thoroughly analyzed using nonlinear dynamics tools, including bifurcation diagrams, Lyapunov exponent spectra, phase portraits, time-domain waveforms, and basins of attraction. To validate its practicality and reliability, a hardware platform is created using a Microcontroller Unit (MCU), and hardware experiments confirm the model’s complex dynamic behaviors. Based on this model, an efficient IoMT data privacy protection method is designed by utilizing the complex chaotic properties of the dual-memristor brain-like chaotic neural network. A comprehensive security analysis of the encryption of colored medical image data is also performed. Results and Discussions The results demonstrate that the proposed network not only exhibits complex grid-like multi-structure chaotic attractors but also possesses the capability to regulate planar initial condition displacements, significantly enhancing its potential for cryptographic applications. Experimental findings indicate that this method performs exceptionally well across key metrics, including a large key space, low pixel correlation, high key sensitivity, and strong robustness against noise and data loss attacks. Conclusions This study presents an innovative and effective solution for protecting medical data privacy in IoMT environments, providing a solid foundation for the development of secure technologies in intelligent healthcare systems.
- Memristor,
- Chaotic system,
- Hopfield neural network,
- Multi-attractors,
- Chaotic encryption

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References(37)

References

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