Explainable ML-Based Intrusion Detection for E-Government Cyber Defence

This repository contains the source code, experimental notebooks, datasets/results, and supplementary materials associated with the research study: "Explainable Machine Learning-Based Intrusion Detection for e-Government Cyber Defence" The study investigates the use of explainable and trustworthy machine learning techniques for network intrusion detection, with emphasis on detection performance, probability calibration, and model interpretability.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-28
DOI
https://doi.org/10.5281/zenodo.22148786
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
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article

Explainable ML-Based Intrusion Detection for E-Government Cyber Defence

Islam Md Ashraful
Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
article

Explainable ML-Based Intrusion Detection for E-Government Cyber Defence

Islam Md Ashraful
article en

Abstract

This repository contains the source code, experimental notebooks, datasets/results, and supplementary materials associated with the research study: "Explainable Machine Learning-Based Intrusion Detection for e-Government Cyber Defence" The study investigates the use of explainable and trustworthy machine learning techniques for network intrusion detection, with emphasis on detection performance, probability calibration, and model interpretability.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 8%
Explainable Artificial Intelligence (XAI)
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