Explainable Artificial Intelligence (AI) for Intrusion Detection Systems: LIME and SHAP Applicability on Multi-Layer Perceptron

Abstract

Intrusion Detection Systems (IDS) are critical for safeguarding network security by identifying malicious activities in real-time. While Multi-Layer Perceptron (MLP) neural networks have demonstrated high accuracy in detecting intrusions, their complex decision-making processes remain largely opaque, limiting their practical adoption. This study explores the application of Explainable Artificial Intelligence (XAI) techniques—specifically LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations)—to enhance the interpretability of MLP-based IDS. By providing transparent and interpretable explanations of individual intrusion predictions, these methods help security analysts understand, trust, and effectively respond to alerts generated by the system. The comparative analysis highlights the strengths and limitations of LIME and SHAP in terms of explanation quality, computational efficiency, and applicability in real-world intrusion detection scenarios. The integration of XAI with MLP models promises to bridge the gap between high-performance detection and explainability, advancing the development of trustworthy cybersecurity solutions.