High-dimensional memristive neural network and its application in commercial data encryption communication
With the development of economic globalization, demands for digital business data sharing among different subsidiary companies are constantly emerging. How to achieve secure transmission of commercial data in the Internet of Things (IoT) is a challenging task. In this paper, we propose a memristive...
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| Published in | Expert systems with applications Vol. 242; p. 122513 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.05.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | With the development of economic globalization, demands for digital business data sharing among different subsidiary companies are constantly emerging. How to achieve secure transmission of commercial data in the Internet of Things (IoT) is a challenging task. In this paper, we propose a memristive neural network-based method for encrypted communication of commercial data. First, a high-dimensional memristive Hopfield neural network (MMHNN) model with three memristive synapses and seven neurons is proposed and its complex dynamical behaviors are deeply analyzed. The results of theoretical analysis and numerical simulations show that the proposed MMHNN can generate quasi-periodic, periodic, chaotic and hyperchaotic attractors, as well as some controllable 1-direction (1D), 2D, 3D hyperchaotic multi-scroll attractors and other types of parametric regulatory dynamics and initial value regulation dynamics. In addition, an analog equivalent circuit for the MMHNN is constructed, and the circuit simulations experimental results demonstrate the correctness and feasibility of the multi-scroll attractors phenomena. Finally, a novel MMHNN-based encryption scheme for IoT commercial data images is proposed by applying hyperchaotic spatial multi-scroll attractors, and the performance analysis shows that the proposed system achieves good encryption performances, especially in terms of keyspace, information entropy, correlation and differential attacks. The hardware experiments further verify the effectiveness and superiority of the proposed commercial data encryption scheme based on the MMHNN.
•A high-dimensional memristive neural network model is proposed.•Multidirectional hyperchaotic multi-scroll attractors are found.•The analog memristive neural network circuit is designed and implemented.•A commercial data encryption solution is designed. |
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| AbstractList | With the development of economic globalization, demands for digital business data sharing among different subsidiary companies are constantly emerging. How to achieve secure transmission of commercial data in the Internet of Things (IoT) is a challenging task. In this paper, we propose a memristive neural network-based method for encrypted communication of commercial data. First, a high-dimensional memristive Hopfield neural network (MMHNN) model with three memristive synapses and seven neurons is proposed and its complex dynamical behaviors are deeply analyzed. The results of theoretical analysis and numerical simulations show that the proposed MMHNN can generate quasi-periodic, periodic, chaotic and hyperchaotic attractors, as well as some controllable 1-direction (1D), 2D, 3D hyperchaotic multi-scroll attractors and other types of parametric regulatory dynamics and initial value regulation dynamics. In addition, an analog equivalent circuit for the MMHNN is constructed, and the circuit simulations experimental results demonstrate the correctness and feasibility of the multi-scroll attractors phenomena. Finally, a novel MMHNN-based encryption scheme for IoT commercial data images is proposed by applying hyperchaotic spatial multi-scroll attractors, and the performance analysis shows that the proposed system achieves good encryption performances, especially in terms of keyspace, information entropy, correlation and differential attacks. The hardware experiments further verify the effectiveness and superiority of the proposed commercial data encryption scheme based on the MMHNN.
•A high-dimensional memristive neural network model is proposed.•Multidirectional hyperchaotic multi-scroll attractors are found.•The analog memristive neural network circuit is designed and implemented.•A commercial data encryption solution is designed. |
| ArticleNumber | 122513 |
| Author | Wang, Chunhua Lin, Hairong Tang, Dong Sun, Yichuang Yu, Fei |
| Author_xml | – sequence: 1 givenname: Chunhua orcidid: 0000-0001-6522-9795 surname: Wang fullname: Wang, Chunhua email: wch1227164@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China – sequence: 2 givenname: Dong orcidid: 0009-0004-4186-3678 surname: Tang fullname: Tang, Dong email: tdong@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China – sequence: 3 givenname: Hairong orcidid: 0000-0003-3506-9780 surname: Lin fullname: Lin, Hairong email: haironglin@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China – sequence: 4 givenname: Fei surname: Yu fullname: Yu, Fei email: yufeiyfyf@csust.edu.cn organization: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China – sequence: 5 givenname: Yichuang surname: Sun fullname: Sun, Yichuang email: y.sun@herts.ac.uk organization: School of Engineering and Computer Science, University of Hertfordshire, AL109AB Hatfield, UK |
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| Keywords | Commercial image encryption Hyperchaotic multi-scroll attractors Memristive neural networks Multi-stability IoT application |
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| Snippet | With the development of economic globalization, demands for digital business data sharing among different subsidiary companies are constantly emerging. How to... |
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| SubjectTerms | Commercial image encryption Hyperchaotic multi-scroll attractors IoT application Memristive neural networks Multi-stability |
| Title | High-dimensional memristive neural network and its application in commercial data encryption communication |
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