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

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dc.contributor.authorAl Ghuraibawi, Adnan Hasan Bdair
dc.date.accessioned2025-10-16T07:34:59Z
dc.date.available2025-10-16T07:34:59Z
dc.date.issued2023-12
dc.description.abstractThe 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.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/22839
dc.language.isoen
dc.subjectIntrusion detection systems (Computer security)
dc.titleA Feature Selection Approach Based On Hybridizing Flower Pollination Algorithm With Particle Swarm Optimization For Enhancing The Performance Of Ipv6 Intrusion Detection System
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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