# Machine Unlearning for the XGBoost Model with Network Intrusion Datasets

arXiv: https://arxiv.org/abs/2606.19220 · HTML: https://mlsanity.com/paper/2606.19220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 8 of 8)

- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Machine Unlearning for Random Forests](https://mlsanity.com/paper/2009.05567/) (2020)
- [Machine Unlearning: Solutions and Challenges](https://mlsanity.com/paper/2308.07061/) (2023)
- [Membership Inference via Backdooring](https://mlsanity.com/paper/2206.04823/) (2022)
- [Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2406.16257/) (2024)
- [Network Intrusion Datasets: A Survey, Limitations, and Recommendations](https://mlsanity.com/paper/2502.06688/) (2025)
- [Flow Exporter Impact on Intelligent Intrusion Detection Systems](https://mlsanity.com/paper/2412.14021/) (2024)

## Abstract (arXiv)

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.