Tool
Explainable and Uncertainty-Aware Machine Learning for Reliable Network Intrusion Detection: Calibration, Conformal Prediction, and Cross-Dataset Generalization
Code and measurement logs for the paper "Explainable and Uncertainty-Aware Machine Learning for Reliable Network Intrusion Detection: Calibration, Conformal Prediction, and Cross-Dataset Generalization." The scripts reproduce the main results (RQ1-RQ6): detection, imbalance-aware calibration (attack-region ECE), split/Mondrian conformal prediction, SHAP explainability, and a confound-free cross-dataset generalization study on the unified NetFlow v2 datasets (NF-UNSW-NB15-v2, NF-CSE-CIC-IDS2018-v2). Raw datasets are not included; see README for sources. Licensed under MIT. Version 1.0.1 adds scripts missing from 1.0.0 (see README, "Changes in
Sugiantoro, Bambang, Karim, Abdul
mit-licenseView resource