Please use this identifier to cite or link to this item:
http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31073| Title: | Robost Network Anomaly Detection through meta- Ensemble Learning Comparative Evaluation of Nine Classifier's |
| Authors: | Temple Chukwudi Okeahialam, Njoku Donatus Onyedikachi Okeahialam Amarachukwu Hossana, Okechukwu A Amaefule |
| Keywords: | Network security, Anomaly Detection ensembly learning machine learning applications intrusion detection systems |
| Issue Date: | 21-Apr-2026 |
| Publisher: | International journal of Applied science and Engineering Technology |
| Series/Report no.: | Volume 16;I20-137 |
| 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. |
| URI: | http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31073 |
| ISSN: | 3940 |
| Appears in Collections: | Information and Media Technology |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Networks 11.pdf | Robost comparative evaluation of Nine Classifier's network Anomaly Detection through meta-Ensemble Learning | 955.35 kB | Adobe PDF | View/Open |
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