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http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31075| Title: | Rbust network Anomaly Detection through meta-Ensemble Learning Comparative Evaluation of Eight Classifier's |
| Authors: | Ngozi Ukamaka Okonkwo, Calistus Tochukwu Ikwazom Grace Amina Onyeabor, Jane Ada Ukaigwe Temple Okeahialam C |
| Keywords: | Network security, Anomaly Detection ensembly learning machine learning applications intrusion detection systems |
| Issue Date: | 20-Feb-2026 |
| Publisher: | IJPE |
| Citation: | Good |
| Series/Report no.: | Volume 22;PP 119-127 |
| 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. |
| URI: | http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31075 |
| ISSN: | 23940 IJPE |
| Appears in Collections: | Information and Media Technology |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| NetworksAnomalies_MetaEnsemble-1.pdf | 963.04 kB | Adobe PDF | View/Open |
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