Advances in Ensemble Machine Learning for Network Intrusion Detection Systems: A Comprehensive Review | ||||
| Benha Journal of Engineering Science and Technology | ||||
| Volume 2, Issue 1, July 2025, Page 117-125 PDF (435.77 K) | ||||
| Document Type: Research Article | ||||
| DOI: 10.21608/bjest.2025.445861 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohmed A. Salama* ; Radwa M. Tawfeek; Sara Hamdy; Omar M. Salim | ||||
| Electrical Engineering Department, Benha Faculty of Engineering, Benha University, Benha, Egypt | ||||
| Abstract | ||||
| As cyber threats grow increasingly sophisticated, robust network security demands adaptive intrusion detection systems (IDS). Traditional machine learning-based IDS often struggle with high false alarm rates and poor generalization to emerging attacks, while deep learning-based IDS offer high detection accuracy but require significant computational resources. Ensemble learning techniques provide an effective balance between efficiency and accuracy, improving detection through model diversity and decision aggregation. This review explores ensemble-based intrusion detection systems, emphasizing diverse aggregation techniques, including homogeneous and heterogeneous ensemble methods. It provides an in-depth analysis of feature selection strategies, data balancing techniques, and classification models, offering a comparative assessment across benchmark datasets. Additionally, the study highlights key challenges and outlines future research directions to advance ensemble learning in network intrusion detection. | ||||
| Keywords | ||||
| Ensemble Learning; NIDS; Feature Selection; Machine Learning; Data Imbalance | ||||
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