XGBoost-Forget applies machine unlearning to XGBoost on IoT-23 and GeNIS network intrusion datasets, achieving faster forgetting with maintained predictive performance.
Network intrusion datasets: a survey, limitations, and recommendations,
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Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.
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Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
XGBoost-Forget applies machine unlearning to XGBoost on IoT-23 and GeNIS network intrusion datasets, achieving faster forgetting with maintained predictive performance.