Abstract

The implementation of Intrusion Detection Systems (IDS) remains crucial for network security yet high-dimensional data alongside class imbalance issues decrease their functionality. Machine learning-based IDS models, which use traditional approaches experience difficulties in providing explanations about their prediction results. An IDS framework enhancement with explainable AI (XAI) methods aims at improving the system's transparency throughout this study. The data processing includes KNN imputation combined with K-Means SMOTE to handle missing information and class imbalance problems. When selecting features the model uses a merged methodology combining Pearson Correlation with Mutual Information and Sequential Forward Floating Selection (SFFS) algorithms for optimization. Light Gradient Boosting Model (LGBM) serves as the classification model that produces higher accuracy than competing methods with 90.71% for UNSW-NB 15 and 96.98% for CICIDS-2017. By using SHAP-based explain ability, the system provides worldwide and specific model interpretations that enable users to trust IDS prediction results. The experimental findings validate that the proposed methodology succeeds in simplifying the system while improving its classification functionality and delivering stronger interpretability properties to tackle weaknesses of current IDS technologies. The examination presents important findings for the development of secure network protection technologies, which operate with transparency.