Robost Network Anomaly Detection through meta- Ensemble Learning Comparative Evaluation of Nine Classifier's

Please use this identifier to cite or link to this item: http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31073

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DC Field	Value	Language
dc.contributor.author	Temple Chukwudi Okeahialam, Njoku Donatus Onyedikachi	-
dc.contributor.author	Okeahialam Amarachukwu Hossana, Okechukwu A Amaefule	-
dc.date.accessioned	2026-05-12T13:57:28Z	-
dc.date.available	2026-05-12T13:57:28Z	-
dc.date.issued	2026-04-21	-
dc.identifier.issn	3940	-
dc.identifier.uri	http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31073	-
dc.description.abstract	Effective detection of network anomalies is crucial when it comes to security of computer networks but traditional methods tend to fail when used to address a wide range of traffic and dynamiiccaly changing conditions. This paper provides the nine ensemble algorithms such as the random Forest, Extra.	en_US
dc.description.sponsorship	Self sponsor	en_US
dc.language.iso	en	en_US
dc.publisher	International journal of Applied science and Engineering Technology	en_US
dc.relation.ispartofseries	Volume 16;I20-137	-
dc.subject	Network security, Anomaly Detection ensembly learning machine learning applications intrusion detection systems	en_US
dc.title	Robost Network Anomaly Detection through meta- Ensemble Learning Comparative Evaluation of Nine Classifier's	en_US
dc.type	Article	en_US
Appears in Collections:	Information and Media Technology

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