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Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models
The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature...
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/PMC10489985/ https://www.ncbi.nlm.nih.gov/pubmed/37687798 http://dx.doi.org/10.3390/s23177342 |
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author | Rihan , Shaza Dawood Ahmed Anbar , Mohammed Alabsi, Basim Ahmad |
author_facet | Rihan , Shaza Dawood Ahmed Anbar , Mohammed Alabsi, Basim Ahmad |
author_sort | Rihan , Shaza Dawood Ahmed |
collection | PubMed |
description | The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models. Ensemble feature selection combines filter techniques such as variance threshold, mutual information, Chi-square, ANOVA, and L1-based methods. By leveraging the strengths of each technique, the ensemble is formed by the union of selected features. However, this union operation may overlook redundancy and irrelevance, potentially leading to a larger feature set. To address this, a wrapper algorithm called Recursive Feature Elimination (RFE) is applied to refine the feature selection. The impact of the selected feature set on the performance of Deep Learning (DL) models (CNN, RNN, GRU, and LSTM) is evaluated using the IoT-Botnet 2020 dataset, considering detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR). All DL models achieved the highest detection accuracy, precision, recall, and F1 measure values, ranging from 97.05% to 97.87%, 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively. |
format | Online Article Text |
id | pubmed-10489985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104899852023-09-09 Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models Rihan , Shaza Dawood Ahmed Anbar , Mohammed Alabsi, Basim Ahmad Sensors (Basel) Article The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models. Ensemble feature selection combines filter techniques such as variance threshold, mutual information, Chi-square, ANOVA, and L1-based methods. By leveraging the strengths of each technique, the ensemble is formed by the union of selected features. However, this union operation may overlook redundancy and irrelevance, potentially leading to a larger feature set. To address this, a wrapper algorithm called Recursive Feature Elimination (RFE) is applied to refine the feature selection. The impact of the selected feature set on the performance of Deep Learning (DL) models (CNN, RNN, GRU, and LSTM) is evaluated using the IoT-Botnet 2020 dataset, considering detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR). All DL models achieved the highest detection accuracy, precision, recall, and F1 measure values, ranging from 97.05% to 97.87%, 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively. MDPI 2023-08-23 /pmc/articles/PMC10489985/ /pubmed/37687798 http://dx.doi.org/10.3390/s23177342 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 Rihan , Shaza Dawood Ahmed Anbar , Mohammed Alabsi, Basim Ahmad Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title | Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title_full | Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title_fullStr | Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title_full_unstemmed | Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title_short | Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models |
title_sort | approach for detecting attacks on iot networks based on ensemble feature selection and deep learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10489985/ https://www.ncbi.nlm.nih.gov/pubmed/37687798 http://dx.doi.org/10.3390/s23177342 |
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