Explainable Artificial Intelligence for Enhancing Intrusion Detection Systems: A Comprehensive Framework for Transparent Network Security

Abstract

This research presents a comprehensive framework for integrating Explainable Artificial Intelligence (XAI) into Intrusion Detection Systems (IDS) to address the critical challenge of AI model opacity in cybersecurity. Traditional AI-based IDS models function as "black boxes," limiting trust, accountability, and practical deployment. The proposed XAI-IDS framework combines machine learning-based intrusion detection (Random Forest, XGBoost, Neural Networks) with explainability mechanisms including SHAP, LIME, and feature importance analysis. Experimental evaluation using benchmark datasets (KDD Cup 99, UNSW-NB15, CIC-IDS-2017) demonstrates that the XAI-enabled system achieves high detection accuracy (96-98%) while providing transparent, human-interpretable explanations for each security decision. The framework significantly reduces false positives, enhances auditability, and improves security analyst trust and response effectiveness without substantial performance degradation.