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Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security
In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plague...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675451/ https://www.ncbi.nlm.nih.gov/pubmed/38005679 http://dx.doi.org/10.3390/s23229294 |
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author | Krishnasamy, Sundaramoorthy Alotaibi, Mutlaq B. Alehaideb, Lolwah I. Abbas, Qaisar |
author_facet | Krishnasamy, Sundaramoorthy Alotaibi, Mutlaq B. Alehaideb, Lolwah I. Abbas, Qaisar |
author_sort | Krishnasamy, Sundaramoorthy |
collection | PubMed |
description | In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F(3)S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks. |
format | Online Article Text |
id | pubmed-10675451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106754512023-11-20 Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security Krishnasamy, Sundaramoorthy Alotaibi, Mutlaq B. Alehaideb, Lolwah I. Abbas, Qaisar Sensors (Basel) Article In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F(3)S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks. MDPI 2023-11-20 /pmc/articles/PMC10675451/ /pubmed/38005679 http://dx.doi.org/10.3390/s23229294 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 Krishnasamy, Sundaramoorthy Alotaibi, Mutlaq B. Alehaideb, Lolwah I. Abbas, Qaisar Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title | Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title_full | Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title_fullStr | Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title_full_unstemmed | Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title_short | Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security |
title_sort | development and validation of a cyber-physical system leveraging efdpn for enhanced wsn-iot network security |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675451/ https://www.ncbi.nlm.nih.gov/pubmed/38005679 http://dx.doi.org/10.3390/s23229294 |
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