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A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle...

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Detalles Bibliográficos
Autores principales: Bahaa, Ahmed, Sayed, Abdalla, Elfangary, Laila, Fahmy, Hanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714761/
https://www.ncbi.nlm.nih.gov/pubmed/36454861
http://dx.doi.org/10.1371/journal.pone.0278493
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author Bahaa, Ahmed
Sayed, Abdalla
Elfangary, Laila
Fahmy, Hanan
author_facet Bahaa, Ahmed
Sayed, Abdalla
Elfangary, Laila
Fahmy, Hanan
author_sort Bahaa, Ahmed
collection PubMed
description Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.
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spelling pubmed-97147612022-12-02 A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach Bahaa, Ahmed Sayed, Abdalla Elfangary, Laila Fahmy, Hanan PLoS One Research Article Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms. Public Library of Science 2022-12-01 /pmc/articles/PMC9714761/ /pubmed/36454861 http://dx.doi.org/10.1371/journal.pone.0278493 Text en © 2022 Bahaa et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bahaa, Ahmed
Sayed, Abdalla
Elfangary, Laila
Fahmy, Hanan
A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title_full A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title_fullStr A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title_full_unstemmed A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title_short A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach
title_sort novel hybrid optimization enabled robust cnn algorithm for an iot network intrusion detection approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714761/
https://www.ncbi.nlm.nih.gov/pubmed/36454861
http://dx.doi.org/10.1371/journal.pone.0278493
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