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Secured node detection technique based on artificial neural network for wireless sensor network

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dc.citedby8
dc.contributor.authorHasan B.en_US
dc.contributor.authorAlani S.en_US
dc.contributor.authorSaad M.A.en_US
dc.contributor.authorid57218909003en_US
dc.contributor.authorid57195407170en_US
dc.contributor.authorid57211413695en_US
dc.date.accessioned2023-05-29T09:09:18Z
dc.date.available2023-05-29T09:09:18Z
dc.date.issued2021
dc.description.abstractThe wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network. � 2021 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijece.v11i1.pp536-544
dc.identifier.epage544
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85091140084
dc.identifier.spage536
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091140084&doi=10.11591%2fijece.v11i1.pp536-544&partnerID=40&md5=cd00ccbe1ae86a7eeb08f97665b38c80
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26341
dc.identifier.volume11
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Electrical and Computer Engineering
dc.titleSecured node detection technique based on artificial neural network for wireless sensor networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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