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.
An in-depth experimen- tal study of anomaly detection using gradient boosted machine,
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Hybrid blacklist plus MLP on 16 structural URL features reaches 99.24% accuracy and 1.2 ms inference on the PhiUSIIL dataset of 235k URLs, outperforming several tree-based models.
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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.
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A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features
Hybrid blacklist plus MLP on 16 structural URL features reaches 99.24% accuracy and 1.2 ms inference on the PhiUSIIL dataset of 235k URLs, outperforming several tree-based models.