Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in in...
Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems
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Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
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 Shapley Additive explanations feature selection, to improv...
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Full title
Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems
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TN_cdi_doaj_primary_oai_doaj_org_article_2dcccc3e43684c2b843bfff13c17385c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_2dcccc3e43684c2b843bfff13c17385c
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-81151-1
