Implementation of synchronization of multifractional- order of chaotic neural networks with a variety of multi-time-delays: Studying the effect of double encryption for text encryption

• Main Authors: Latiff, Fatin Nabila Abd, Othman, Wan Ainun Mior
• Format: Article
• Published: Public Library of Science 2022
• Subjects: QA Mathematics
• Online Access: http://eprints.um.edu.my/43622/

This research proposes the idea of double encryption, which is the combination of chaos synchronization of non-identical multi-fractional-order neural networks with multi-timedelays (FONNSMD) and symmetric encryption. Symmetric encryption is well known to be outstanding in speed and accuracy but less effective. Therefore, to increase the strength of data protection effectively, we combine both methods where the secret keys are generated from the third part of the neural network systems (NNS) and used only once to encrypt and decrypt the message. In addition, a fractional-order Lyapunov direct function (FOLDF) is designed and implemented in sliding mode control systems (SMCS) to maintain the convergence of approximated synchronization errors. Finally, three examples are carried out to confirm the theoretical analysis and find which synchronization is achieved. Then the result is combined with symmetric encryption to increase the security of secure communication, and a numerical simulation verifies the method's accuracy. © 2022 Abd Latiff, Mior Othman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.