{"title":"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","authors":"Arindam Sarkar, Moirangthem Marjit Singh, Hanjabam Saratchandra Sharma","doi":"10.1007/s13042-024-02310-4","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"20 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial recurrent neural network coordinated secured transmission towards safeguarding confidentiality in smart Industrial Internet of Things\",\"authors\":\"Arindam Sarkar, Moirangthem Marjit Singh, Hanjabam Saratchandra Sharma\",\"doi\":\"10.1007/s13042-024-02310-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":51327,\"journal\":{\"name\":\"International Journal of Machine Learning and Cybernetics\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Machine Learning and Cybernetics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s13042-024-02310-4\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Learning and Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13042-024-02310-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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