人工递归神经网络协调安全传输,保障智能工业物联网的机密性

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Machine Learning and Cybernetics Pub Date : 2024-08-28 DOI:10.1007/s13042-024-02310-4
Arindam Sarkar, Moirangthem Marjit Singh, Hanjabam Saratchandra Sharma
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引用次数: 0

摘要

本研究介绍了一种新方法来解决工业物联网(IIoT)中的加密密钥交换问题。这项研究的重点是传统加密密钥交换算法效率低下、评估程序冗长,不适合快速且不断变化的 IIoT 设备环境。在解决方案领域,所提出的方法利用神经网络与矢量估值和递归神经网络(RNN)的同步,融合驱动-响应机制,以提高关键操作的速度和效率。研究探讨了延迟对生成任意输入的影响,以及在结合了驱动-响应机制的 RNN 中进行同步输入向量创建所面临的协调挑战。本文通过利用 RNN 框架构建用于共享会话密钥的 ANN,解释了对人工神经网络(ANN)中协调性的基本评估。这项研究做出了多方面的贡献:(1) 使用多项式协调技术为使用 RNN 的 ANN 同步过程生成协调输入,(2) 使用 Lyapunov 公式和不等式评估方法确定所需的控制参数和时变条件,以便在使用多项式和非多项式函数提出的驱动响应系统中实现同步、(3) 通过数值图解证明多项式同步与非多项式同步之间的联系,以及 (4) 设计 ANN 的对称布局,以便在 IIoT 网络中创建会话密钥。所建议的技术优于文献中的现有方法,为加密密钥交换提供了更快速、更可靠的解决方案,为改进工业应用并确保其安全性铺平了道路。这种新方法不仅解决了当前的低效问题,还为未来改进 IIoT 环境中的安全通信铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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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来源期刊
International Journal of Machine Learning and Cybernetics
International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.90
自引率
10.70%
发文量
225
期刊介绍: Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: Machine Learning for modeling interactions between systems Pattern Recognition technology to support discovery of system-environment interaction Control of system-environment interactions Biochemical interaction in biological and biologically-inspired systems Learning for improvement of communication schemes between systems
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