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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A three-component fusion architecture of LSTM, statistical, and graph modules detects fraud and AML on synthetic banking data with F1 scores of 0.787 (transactions) and 0.867 (sessions), outperforming rule-based and LSTM-only baselines.
citing papers explorer
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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.
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An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
A three-component fusion architecture of LSTM, statistical, and graph modules detects fraud and AML on synthetic banking data with F1 scores of 0.787 (transactions) and 0.867 (sessions), outperforming rule-based and LSTM-only baselines.