EXPLAINABLE MACHINE LEARNING FOR NETWORK INTRUSION DETECTION USING LIGHTGBM AND SHAP: AN IMPLEMENTATION STUDY ON HIKARI-2021
Machine-learning intrusion detection systems can provide strong predictive performance while offering limited evidence for why an individual network-flow record was classified as suspicious. This study examines the integration of SHapley Additive exPlanations (SHAP) with a LightGBM-based network intrusion detector trai...