Experimental evaluation demonstrates high detection accuracy and transparent explanations across benchmark datasets, indicating improved trust and reduced false positives in network security.
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.
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Muhammad Haris Khan (2026) studied this question.