提出XGBoost-Forget，实现快速删除特定数据点的机器遗忘。
Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
- 基于梯度更新机制重构模型，避免全量重训练。
- 在两个网络入侵数据集上保持接近原始模型性能。
- 适合需要高效数据删除的表格型入侵检测场景。
机器遗忘（MU）作为一种无需重新训练即可移除特定数据点的技术日益重要。然而，现有研究多集中于深度学习与图像数据，缺乏对依赖表格数据的网络入侵检测领域的关注。本文提出XGBoost-Forget方法，针对XGBoost模型设计了专门的遗忘机制，并在两个表格型网络入侵数据集IoT-23和GeNIS上进行评估，采用多种指标衡量模型性能、遗忘效率与遗忘质量。实验结果表明，该方法在保持预测性能接近原始模型的同时，显著提升了遗忘速度，展现了其在表格型网络入侵检测场景中应用的潜力。
原文摘要 · Abstract (English)
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.
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