A novel bagging- XGBoost ensemble model for attaining high accuracy and computational efficiency in network intrusion detection
The study focuses on enhancing network intrusion detection to enhance network security and prevent potential data breaches. We propose B-XGBoost, an ensemble learning model that combines bagging and boosting, using 10k cross-validation and Bayesian optimization for binary network intrusion classific...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/31/e3sconf_iccsei2023_01007.pdf |