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Artificial recurrent neural network coordinated secured transmission towards safeguarding confidentiality in smart Industrial Internet of Things

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Abstract

This research introduces a new method to tackle the issue of exchanging cryptographic keys in the Industrial Internet of Things (IIoT). This study focuses on the inefficiency and lengthy evaluation procedures of conventional cryptographic key exchange algorithms, which are not appropriate for the rapid and constantly changing IIoT device environment. In the solution domain, the proposed approach uses synchronization of neural networks with vector valued and Recurrent Neural Networks (RNNs), merging drive-response mechanisms to enhance speed and efficiency in crucial operations. The research examines the influence of postponements on the generating arbitrary inputs and coordination challenges in RNNs that incorporate drive-response mechanisms for synchronized input vector creation. This article explains an elementary evaluation of coordination in Artificial Neural Networks (ANNs) by utilizing an RNN framework to structure ANNs for sharing session keys. The study provides multiple contributions: (1) employing the polynomial coordination technique to generate coordinated inputs for the ANN synchronization process using RNNs, (2) using Lyapunov formulas and inequality assessment methods to identify required control parameters and time-varying conditions for achieving synchronization in the drive-response systems proposed with polynomial and non-polynomial functions, (3) demonstrating the connection between polynomial and non-polynomial synchronization with numerical illustrations, and (4) designing symmetric layouts of ANNs to create a session keys in the IIoT network. The suggested technique outperforms existing methods in the literature by offering a quicker, more dependable solution for cryptographic key exchange, paving the way for improved and secure industrial applications. This new method not only fixes current inefficiencies but also paves the way for future improvements in secure communication in the IIoT environment.

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Acknowledgements

This work was supported by DBT STAR College scheme of Ramakrishna Mission Vidyamandira.

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Authors and Affiliations

  1. Department of Computer Science and Electronics, Ramakrishna Mission Vidyamandira, Belur Math, Howrah, West Bengal, 711202, India

    Arindam Sarkar

  2. Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh, 791109, India

    Moirangthem Marjit Singh & Hanjabam Saratchandra Sharma

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Sarkar, A., Singh, M.M. & Sharma, H.S. Artificial recurrent neural network coordinated secured transmission towards safeguarding confidentiality in smart Industrial Internet of Things. Int. J. Mach. Learn. & Cyber. 16, 891–917 (2025). https://doi.org/10.1007/s13042-024-02310-4

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