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Hybridized bio-inspired intrusion detection system for Internet of Things

The Internet of Things (IoT) consists of several smart devices equipped with computing, sensing, and network capabilities, which enable them to collect and exchange heterogeneous data wirelessly. The increasing usage of IoT devices in daily activities increases the security needs of IoT systems. The...

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Detalles Bibliográficos
Autores principales: Singh, Richa, Ujjwal, R. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929138/
https://www.ncbi.nlm.nih.gov/pubmed/36818821
http://dx.doi.org/10.3389/fdata.2023.1081466
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author Singh, Richa
Ujjwal, R. L.
author_facet Singh, Richa
Ujjwal, R. L.
author_sort Singh, Richa
collection PubMed
description The Internet of Things (IoT) consists of several smart devices equipped with computing, sensing, and network capabilities, which enable them to collect and exchange heterogeneous data wirelessly. The increasing usage of IoT devices in daily activities increases the security needs of IoT systems. These IoT devices are an easy target for intruders to perform malicious activities and make the underlying network corrupt. Hence, this paper proposes a hybridized bio-inspired-based intrusion detection system (IDS) for the IoT framework. The hybridized sine-cosine algorithm (SCA) and salp swarm algorithm (SSA) determines the essential features of the network traffic. Selected features are passed to a machine learning (ML) classifier for the detection and classification of intrusive traffic. The IoT network intrusion dataset determines the performance of the proposed system in a python environment. The proposed hybridized system achieves maximum accuracy of 84.75% with minimum selected features i.e., 8 and takes minimum time of 96.42 s in detecting intrusion for the IoT network. The proposed system's effectiveness is shown by comparing it with other similar approaches for performing multiclass classification.
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spelling pubmed-99291382023-02-16 Hybridized bio-inspired intrusion detection system for Internet of Things Singh, Richa Ujjwal, R. L. Front Big Data Big Data The Internet of Things (IoT) consists of several smart devices equipped with computing, sensing, and network capabilities, which enable them to collect and exchange heterogeneous data wirelessly. The increasing usage of IoT devices in daily activities increases the security needs of IoT systems. These IoT devices are an easy target for intruders to perform malicious activities and make the underlying network corrupt. Hence, this paper proposes a hybridized bio-inspired-based intrusion detection system (IDS) for the IoT framework. The hybridized sine-cosine algorithm (SCA) and salp swarm algorithm (SSA) determines the essential features of the network traffic. Selected features are passed to a machine learning (ML) classifier for the detection and classification of intrusive traffic. The IoT network intrusion dataset determines the performance of the proposed system in a python environment. The proposed hybridized system achieves maximum accuracy of 84.75% with minimum selected features i.e., 8 and takes minimum time of 96.42 s in detecting intrusion for the IoT network. The proposed system's effectiveness is shown by comparing it with other similar approaches for performing multiclass classification. Frontiers Media S.A. 2023-02-01 /pmc/articles/PMC9929138/ /pubmed/36818821 http://dx.doi.org/10.3389/fdata.2023.1081466 Text en Copyright © 2023 Singh and Ujjwal. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Singh, Richa
Ujjwal, R. L.
Hybridized bio-inspired intrusion detection system for Internet of Things
title Hybridized bio-inspired intrusion detection system for Internet of Things
title_full Hybridized bio-inspired intrusion detection system for Internet of Things
title_fullStr Hybridized bio-inspired intrusion detection system for Internet of Things
title_full_unstemmed Hybridized bio-inspired intrusion detection system for Internet of Things
title_short Hybridized bio-inspired intrusion detection system for Internet of Things
title_sort hybridized bio-inspired intrusion detection system for internet of things
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929138/
https://www.ncbi.nlm.nih.gov/pubmed/36818821
http://dx.doi.org/10.3389/fdata.2023.1081466
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