Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems

Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems

Abstract The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including...

Full description

Bibliographic Details
Main Authors: Usman Ahmed, Zheng Jiangbin, Ahmad Almogren, Muhammad Sadiq, Ateeq Ur Rehman, M. T. Sadiq, Jaeyoung Choi
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Bagging and Boosting
Network Security
Intrusion Detection and Prevention Systems
Explainable AI
SHAP Feature Selection
Online Access:https://doi.org/10.1038/s41598-024-81151-1

Internet

https://doi.org/10.1038/s41598-024-81151-1

Similar Items