MDE computes three entropy features from flow stats to match conventional ML performance (F1 0.708-0.989) on four IDS benchmarks while exposing aggregate-metric failures and providing stable SHAP attributions.
Federated learning in intrusion detection: Advancements, applications, and future direc- Page 18 of 20 Research Article Bouke et al., 2026. tions,
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Multi-Level Distributional Entropy for Explainable Network Intrusion Detection
MDE computes three entropy features from flow stats to match conventional ML performance (F1 0.708-0.989) on four IDS benchmarks while exposing aggregate-metric failures and providing stable SHAP attributions.