SHAP-RM : An XAI framework for evaluating machine learning reliability

This research presents a novel framework, SHAP-RM, for assessing the reliability and trustworthiness of machine learning (ML) models in cyber security applications using explainable artificial intelligence (XAI). By applying SHAP (SHapley Additive exPlanations) to XGBoost regression and classification tasks trained on the live PV generation dataset and the UNSW-NB15 dataset, we examine the interpretability and robustness of model decisions. Integrating XAI enhances transparency, offering actionable insights for refining and securing ML-driven defense systems.