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Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment
Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth...
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/PMC10459982/ https://www.ncbi.nlm.nih.gov/pubmed/37631743 http://dx.doi.org/10.3390/s23167207 |
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author | Khadidos, Adil O. AlKubaisy, Zenah Mahmoud Khadidos, Alaa O. Alyoubi, Khaled H. Alshareef, Abdulrhman M. Ragab, Mahmoud |
author_facet | Khadidos, Adil O. AlKubaisy, Zenah Mahmoud Khadidos, Alaa O. Alyoubi, Khaled H. Alshareef, Abdulrhman M. Ragab, Mahmoud |
author_sort | Khadidos, Adil O. |
collection | PubMed |
description | Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures. |
format | Online Article Text |
id | pubmed-10459982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104599822023-08-27 Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment Khadidos, Adil O. AlKubaisy, Zenah Mahmoud Khadidos, Alaa O. Alyoubi, Khaled H. Alshareef, Abdulrhman M. Ragab, Mahmoud Sensors (Basel) Article Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures. MDPI 2023-08-16 /pmc/articles/PMC10459982/ /pubmed/37631743 http://dx.doi.org/10.3390/s23167207 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 Khadidos, Adil O. AlKubaisy, Zenah Mahmoud Khadidos, Alaa O. Alyoubi, Khaled H. Alshareef, Abdulrhman M. Ragab, Mahmoud Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title | Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title_full | Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title_fullStr | Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title_full_unstemmed | Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title_short | Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment |
title_sort | binary hunter–prey optimization with machine learning—based cybersecurity solution on internet of things environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459982/ https://www.ncbi.nlm.nih.gov/pubmed/37631743 http://dx.doi.org/10.3390/s23167207 |
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