Secure exchange of information using artificial intelligence and chaotic system guided neural synchronization

Abstract

In this paper, a chaos-based neural synchronization has been proposed for the development of the public-key exchange protocol. A special neural network structure called Tree Parity Machine (TPM) is used for neural synchronization. Two TPMs accept the common input and different weight vector and update the weights using neural learning rule by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two TPMs become identical. These identical weights serve as a secret key. There is, however, hardly some investigation to investigate the randomness of the common input vector used in the synchronization process. In this paper, logistic Chaos system based Tree Parity Machine (CTPM) is proposed. For faster synchronization, this proposed CTPM model uses logistic chaos generated random common input vector. This proposed CTPM model is faster and has better security than TPM with the same input, output, and hidden neurons. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and robust potential.

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Acknowledgements

The author expressed deep gratitude for the moral and congenial atmosphere support provided by Ramakrishna Mission Vidyamandira, Belur Math, India.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Correspondence to Arindam Sarkar.

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Sarkar, A. Secure exchange of information using artificial intelligence and chaotic system guided neural synchronization. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-021-10554-3

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Keywords

  • Neural synchronization
  • Tree Parity Machine (TPM)
  • Session key
  • Chaos
  • Information