Rbust network Anomaly Detection through meta-Ensemble Learning Comparative Evaluation of Eight Classifier's

Please use this identifier to cite or link to this item: http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31075

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DC Field	Value	Language
dc.contributor.author	Ngozi Ukamaka Okonkwo, Calistus Tochukwu Ikwazom	-
dc.contributor.author	Grace Amina Onyeabor, Jane Ada Ukaigwe	-
dc.contributor.author	Temple Okeahialam C	-
dc.date.accessioned	2026-05-12T15:10:14Z	-
dc.date.available	2026-05-12T15:10:14Z	-
dc.date.issued	2026-02-20	-
dc.identifier.citation	Good	en_US
dc.identifier.issn	23940 IJPE	-
dc.identifier.uri	http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31075	-
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 a systematic review of Eight Ensemble algorithms such as the random Forest, Extra Trees Bagging, AdaBoost, Staking and voting on a dataset of 4,998 samples and 35 features where statistics of network traffic model's based on straightfied 10- Ford cross - Validation.	en_US
dc.description.sponsorship	Self sponsor	en_US
dc.language.iso	en	en_US
dc.publisher	IJPE	en_US
dc.relation.ispartofseries	Volume 22;PP 119-127	-
dc.subject	Network security, Anomaly Detection ensembly learning machine learning applications intrusion detection systems	en_US
dc.title	Rbust network Anomaly Detection through meta-Ensemble Learning Comparative Evaluation of Eight Classifier's	en_US
dc.type	Article	en_US
Appears in Collections:	Information and Media Technology

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