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Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping.

Anqi Hu1,2, Xiaoxue Gong1,2, Lei Guo1,2

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nanan District, Chongqing 400065, China.

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Summary
This summary is machine-generated.

This study introduces a novel joint encryption model using a randomized selective autoencoder neural network (AENN) and chaotic mapping. The model enhances one-dimensional chaos for secure, resource-efficient one-time pad encryption resistant to common attacks.

Keywords:
AENN randomizationcoupled chaos mappingdata encryptionjoint encryptionone-time pad

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Area of Science:

  • Cryptography and Network Security
  • Artificial Intelligence and Machine Learning
  • Chaos Theory and Dynamical Systems

Background:

  • One-dimensional chaos exhibits limitations in period and complexity, hindering its application in secure communication.
  • Existing key synchronization methods for one-time pad encryption can be resource-intensive.
  • There is a need for advanced encryption models that combine chaotic dynamics with neural networks for enhanced security and efficiency.

Purpose of the Study:

  • To propose a randomized selective autoencoder neural network (AENN) and coupled chaotic mapping to overcome limitations of one-dimensional chaos.
  • To develop an improved key synchronization method for one-time pad encryption that conserves channel resources.
  • To introduce a joint encryption model integrating the randomized AENN and chaotic mapping for high-security data transmission.

Main Methods:

  • In-depth analysis of one-dimensional chaos.
  • Development of a randomized selective autoencoder neural network (AENN).
  • Integration of AENN with a novel chaotic coupling mapping for a joint encryption model.
  • Implementation of an improved key synchronization technique for efficient channel resource utilization.

Main Results:

  • The proposed encryption model demonstrates a vast key space and high sensitivity, achieving the effect of one-time pad encryption.
  • Experimental validation confirms the model's ability to resist common cryptanalytic attacks, including exhaustive, selective plaintext, and statistical attacks.
  • The joint encryption model significantly reduces the usage of secure channel resources compared to conventional methods.

Conclusions:

  • The developed joint encryption model offers a robust solution for secure communication by leveraging enhanced chaotic dynamics and AENN.
  • The model provides a high level of security, comparable to one-time pad encryption, while optimizing resource utilization.
  • This approach presents a promising direction for developing next-generation secure communication systems resistant to sophisticated attacks.