presents the Local Interpretable hybrid meta-learner (LI-HML) to address critical challenges in attack detection and network traffic analysis. Key innovations include feature clustering for enhanced diversity, robust handling of imbalanced datasets through bootstrap sampling, and pre- and post-majority voting explanations. The framework not only achieves high performance but also delivers actionable insights into feature importance and decision-making processes. This interpretable and effective approach positions LI-HML as a powerful tool for advancing cybersecurity and network traffic analysis.