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Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping
Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transm...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453204/ https://www.ncbi.nlm.nih.gov/pubmed/37628183 http://dx.doi.org/10.3390/e25081153 |
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author | Hu, Anqi Gong, Xiaoxue Guo, Lei |
author_facet | Hu, Anqi Gong, Xiaoxue Guo, Lei |
author_sort | Hu, Anqi |
collection | PubMed |
description | Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transmission of one-time pad encryption, which can greatly reduce the usage of channel resources. Then, a joint encryption model based on randomized AENN and a new chaotic coupling mapping is proposed. The performance analysis concludes that the encryption model possesses a huge key space and high sensitivity, and achieves the effect of one-time pad encryption. Experimental results show that this model is a high-security joint encryption model that saves secure channel resources and has the ability to resist common attacks, such as exhaustive attacks, selective plaintext attacks, and statistical attacks. |
format | Online Article Text |
id | pubmed-10453204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104532042023-08-26 Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping Hu, Anqi Gong, Xiaoxue Guo, Lei Entropy (Basel) Article Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transmission of one-time pad encryption, which can greatly reduce the usage of channel resources. Then, a joint encryption model based on randomized AENN and a new chaotic coupling mapping is proposed. The performance analysis concludes that the encryption model possesses a huge key space and high sensitivity, and achieves the effect of one-time pad encryption. Experimental results show that this model is a high-security joint encryption model that saves secure channel resources and has the ability to resist common attacks, such as exhaustive attacks, selective plaintext attacks, and statistical attacks. MDPI 2023-08-01 /pmc/articles/PMC10453204/ /pubmed/37628183 http://dx.doi.org/10.3390/e25081153 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Anqi Gong, Xiaoxue Guo, Lei Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title | Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title_full | Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title_fullStr | Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title_full_unstemmed | Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title_short | Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping |
title_sort | joint encryption model based on a randomized autoencoder neural network and coupled chaos mapping |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453204/ https://www.ncbi.nlm.nih.gov/pubmed/37628183 http://dx.doi.org/10.3390/e25081153 |
work_keys_str_mv | AT huanqi jointencryptionmodelbasedonarandomizedautoencoderneuralnetworkandcoupledchaosmapping AT gongxiaoxue jointencryptionmodelbasedonarandomizedautoencoderneuralnetworkandcoupledchaosmapping AT guolei jointencryptionmodelbasedonarandomizedautoencoderneuralnetworkandcoupledchaosmapping |