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An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection
The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962996/ https://www.ncbi.nlm.nih.gov/pubmed/35214297 http://dx.doi.org/10.3390/s22041396 |
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author | Kareem, Saif S. Mostafa, Reham R. Hashim, Fatma A. El-Bakry, Hazem M. |
author_facet | Kareem, Saif S. Mostafa, Reham R. Hashim, Fatma A. El-Bakry, Hazem M. |
author_sort | Kareem, Saif S. |
collection | PubMed |
description | The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions. |
format | Online Article Text |
id | pubmed-8962996 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89629962022-03-30 An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection Kareem, Saif S. Mostafa, Reham R. Hashim, Fatma A. El-Bakry, Hazem M. Sensors (Basel) Article The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions. MDPI 2022-02-11 /pmc/articles/PMC8962996/ /pubmed/35214297 http://dx.doi.org/10.3390/s22041396 Text en © 2022 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 Kareem, Saif S. Mostafa, Reham R. Hashim, Fatma A. El-Bakry, Hazem M. An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title | An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title_full | An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title_fullStr | An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title_full_unstemmed | An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title_short | An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection |
title_sort | effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962996/ https://www.ncbi.nlm.nih.gov/pubmed/35214297 http://dx.doi.org/10.3390/s22041396 |
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