A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System

The proposed approach is evaluated using the “ICMPv6 dataset on different attacks”. The experimental results show that the first proposed approach achieved the best classification accuracy, i.e., 97.96% in terms of the number of features, and it reduced the number of features from 19 to 10 features....

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Main Author: Al Ghuraibawi, Adnan Hasan Bdair
Format: Thesis
Language:English
Published: 2023
Subjects:
Online Access:http://eprints.usm.my/63003/
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author Al Ghuraibawi, Adnan Hasan Bdair
author_facet Al Ghuraibawi, Adnan Hasan Bdair
author_sort Al Ghuraibawi, Adnan Hasan Bdair
description The proposed approach is evaluated using the “ICMPv6 dataset on different attacks”. The experimental results show that the first proposed approach achieved the best classification accuracy, i.e., 97.96% in terms of the number of features, and it reduced the number of features from 19 to 10 features. In addition, the experimental findings demonstrate that the second proposed strategy achieved the best classification accuracy, i.e., 97.99% in terms of the number of characteristics. It reduced the number of features from 19 to 8 features. Finally, the experimental results showed that the third proposed approach achieved the best classification accuracy, i.e., 97.01% in terms of the number of features. It reduced the number of features from 19 to 4 features.
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spelling usm-630032025-10-16T07:40:46Z http://eprints.usm.my/63003/ A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System Al Ghuraibawi, Adnan Hasan Bdair QA75.5-76.95 Electronic computers. Computer science The proposed approach is evaluated using the “ICMPv6 dataset on different attacks”. The experimental results show that the first proposed approach achieved the best classification accuracy, i.e., 97.96% in terms of the number of features, and it reduced the number of features from 19 to 10 features. In addition, the experimental findings demonstrate that the second proposed strategy achieved the best classification accuracy, i.e., 97.99% in terms of the number of characteristics. It reduced the number of features from 19 to 8 features. Finally, the experimental results showed that the third proposed approach achieved the best classification accuracy, i.e., 97.01% in terms of the number of features. It reduced the number of features from 19 to 4 features. 2023-12 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/63003/1/Pages%20from%20ADNAN%20HASAN%20BDAIR%20AL%20GHURAIBAWI%20-%20TESIS.pdf Al Ghuraibawi, Adnan Hasan Bdair (2023) A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Al Ghuraibawi, Adnan Hasan Bdair
A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title_full A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title_fullStr A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title_full_unstemmed A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title_short A Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
title_sort feature selection approach based on hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of ipv6 intrusion detection system
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/63003/
work_keys_str_mv AT alghuraibawiadnanhasanbdair afeatureselectionapproachbasedonhybridizingflowerpollinationalgorithmwithparticleswarmoptimizationforenhancingtheperformanceofipv6intrusiondetectionsystem
AT alghuraibawiadnanhasanbdair featureselectionapproachbasedonhybridizingflowerpollinationalgorithmwithparticleswarmoptimizationforenhancingtheperformanceofipv6intrusiondetectionsystem