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Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment

The Internet of Things (IoT) is a kind of advanced information technology that has grabbed the attention of society. Stimulators and sensors were generally known as smart devices in this ecosystem. In parallel, IoT security provides new challenges. Internet connection and the possibility of communic...

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Autores principales: Annamalai, Chinnappa, Vijayakumaran, Chellavelu, Ponnusamy, Vijayakumar, Kim, Hyunsung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302786/
https://www.ncbi.nlm.nih.gov/pubmed/37420763
http://dx.doi.org/10.3390/s23125596
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author Annamalai, Chinnappa
Vijayakumaran, Chellavelu
Ponnusamy, Vijayakumar
Kim, Hyunsung
author_facet Annamalai, Chinnappa
Vijayakumaran, Chellavelu
Ponnusamy, Vijayakumar
Kim, Hyunsung
author_sort Annamalai, Chinnappa
collection PubMed
description The Internet of Things (IoT) is a kind of advanced information technology that has grabbed the attention of society. Stimulators and sensors were generally known as smart devices in this ecosystem. In parallel, IoT security provides new challenges. Internet connection and the possibility of communication with smart gadgets cause gadgets to indulge in human life. Thus, safety is essential in devising IoT. IoT contains three notable features: intelligent processing, overall perception, and reliable transmission. Due to the IoT span, the security of transmitting data becomes a crucial factor for system security. This study designs a slime mold optimization with ElGamal Encryption with a Hybrid Deep-Learning-Based Classification (SMOEGE-HDL) model in an IoT environment. The proposed SMOEGE-HDL model mainly encompasses two major processes, namely data encryption and data classification. At the initial stage, the SMOEGE technique is applied to encrypt the data in an IoT environment. For optimal key generation in the EGE technique, the SMO algorithm has been utilized. Next, in the later stage, the HDL model is utilized to carry out the classification process. In order to boost the classification performance of the HDL model, the Nadam optimizer is utilized in this study. The experimental validation of the SMOEGE-HDL approach is performed, and the outcomes are inspected under distinct aspects. The proposed approach offers the following scores: 98.50% for specificity, 98.75% for precision, 98.30% for recall, 98.50% for accuracy, and 98.25% for F1-score. This comparative study demonstrated the enhanced performance of the SMOEGE-HDL technique compared to existing techniques.
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spelling pubmed-103027862023-06-29 Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment Annamalai, Chinnappa Vijayakumaran, Chellavelu Ponnusamy, Vijayakumar Kim, Hyunsung Sensors (Basel) Article The Internet of Things (IoT) is a kind of advanced information technology that has grabbed the attention of society. Stimulators and sensors were generally known as smart devices in this ecosystem. In parallel, IoT security provides new challenges. Internet connection and the possibility of communication with smart gadgets cause gadgets to indulge in human life. Thus, safety is essential in devising IoT. IoT contains three notable features: intelligent processing, overall perception, and reliable transmission. Due to the IoT span, the security of transmitting data becomes a crucial factor for system security. This study designs a slime mold optimization with ElGamal Encryption with a Hybrid Deep-Learning-Based Classification (SMOEGE-HDL) model in an IoT environment. The proposed SMOEGE-HDL model mainly encompasses two major processes, namely data encryption and data classification. At the initial stage, the SMOEGE technique is applied to encrypt the data in an IoT environment. For optimal key generation in the EGE technique, the SMO algorithm has been utilized. Next, in the later stage, the HDL model is utilized to carry out the classification process. In order to boost the classification performance of the HDL model, the Nadam optimizer is utilized in this study. The experimental validation of the SMOEGE-HDL approach is performed, and the outcomes are inspected under distinct aspects. The proposed approach offers the following scores: 98.50% for specificity, 98.75% for precision, 98.30% for recall, 98.50% for accuracy, and 98.25% for F1-score. This comparative study demonstrated the enhanced performance of the SMOEGE-HDL technique compared to existing techniques. MDPI 2023-06-15 /pmc/articles/PMC10302786/ /pubmed/37420763 http://dx.doi.org/10.3390/s23125596 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Annamalai, Chinnappa
Vijayakumaran, Chellavelu
Ponnusamy, Vijayakumar
Kim, Hyunsung
Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title_full Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title_fullStr Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title_full_unstemmed Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title_short Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment
title_sort optimal elgamal encryption with hybrid deep-learning-based classification on secure internet of things environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302786/
https://www.ncbi.nlm.nih.gov/pubmed/37420763
http://dx.doi.org/10.3390/s23125596
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