arXiv:2606.19220v1 Announce Type: cross 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 ass
The increasing focus on data privacy regulations (e.g., GDPR, CCPA) and the need for models to forget specific data points necessitates robust 'machine unlearning' techniques.
This work addresses a critical gap in machine unlearning for tabular data, especially relevant for sensitive applications like network intrusion detection, impacting compliance and model integrity.
Machine Unlearning capabilities are extending beyond deep learning and image data to tabular data and established models like XGBoost, broadening its applicability in enterprise AI.
- · Cybersecurity sector
- · Organizations handling sensitive tabular data
- · AI compliance and governance tools
- · XGBoost users
- · Organizations unable to implement unlearning
- · Legacy AI systems lacking unlearning capabilities
Improved compliance for AI systems dealing with personal or sensitive data, particularly in enterprise and security contexts.
Reduced operational overhead for model maintenance and data deletion requests, enabling more agile AI deployment.
Potential for new regulatory frameworks explicitly requiring verifiable machine unlearning in specific domains.
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Read at arXiv cs.AI