Machine Unlearning for the XGBoost Model with Network Intrusion Datasets

2026-06-17

AI Summary

This work addresses the gap in machine unlearning research by introducing XGBoost-Forget, the first unlearning method tailored for XGBoost models in tabular network intrusion detection scenarios. Unlike existing approaches primarily designed for deep learning and image data, XGBoost-Forget efficiently removes specified intrusion data points without requiring full model retraining. The method incorporates a customized unlearning mechanism specifically designed for tabular network traffic data and is validated on real-world datasets such as IoT-23 and GeNIS. Experimental results demonstrate that XGBoost-Forget achieves substantial gains in unlearning efficiency while preserving predictive performance nearly equivalent to that of the original model, thereby offering a practical unlearning solution for security applications driven by tabular data.

Abstract

Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies heavily on tabular data. This work introduces XGBoost-Forget, an unlearning approach for the XGBoost model, to address this gap. The approach is evaluated on two tabular Network Intrusion (NI) datasets, IoT-23 and GeNIS, using multiple metrics to assess model performance, unlearning efficiency, and forgetting quality. The results show that XGBoost-Forget maintains predictive performance close to the original model while providing significantly faster unlearning, demonstrating its potential for MU in tabular NI settings.

Problem

• Machine Unlearning
• XGBoost
• Network Intrusion Detection
• Tabular Data

Innovation

• Machine Unlearning
• XGBoost
• Network Intrusion Detection
• Tabular Data
• XGBoost-Forget