Enhancing Machine Learning Model Interpretability in Intrusion Detection Systems through SHAP Explanations and LLM-Generated Descriptions

Intrusion Detection Systems (IDS) are critical for detecting and mitigating cyber threats, yet the opaqueness of machine learning models used within these systems poses challenges for understanding their decisions. This paper proposes a novel approach to address this issue by integrating SHAP (SHapley Additive exPlanations) values with Large Language Models (LLMs). With the aim of enhancing transp...