TL;DR
Overall, this research contributes a reproducible, leakage-free framework for intelligent intrusion detection, providing a solid foundation for near-real-time cyberattack scoring after offline training and future explainable AI extensions.
This work constructs a web-based Intrusion Detection System prototype by training an entropy-based Decision Tree classifier, conceptually grounded in the C4.5 framework, on the NSL-KDD benchmark.
The findings support the adoption of hybrid IDS architectures as a balanced and practical solution that enhances detection capability, adaptability, and reliability in evolving cyber threat landscapes.
The revised framework extends this four-layer pipeline by explicitly integrating Explainable AI (XAI) and edge-oriented deployment as cross-cutting operational requirements by explicitly integrating Explainable AI (XAI) and edge-oriented deployment as cross-cutting operational requirements.
Existing assessments of machine-learning-based intrusion detection systems (IDSs) often consider each of accuracy, reliability of probability outputs, and transparency of decisions separately. Few attempt to combine all three dimensions in a single assessment. This paper provides a reproducible IDS pipeline that satisf...
A modified multi-stage ensemble feature selection (MMSE-FS) framework that incorporates algorithmic adaptations of Random Forest, Principal Component Analysis, and KBest methods is introduced that substantially reduced the feature space while maintaining high detection performance across both binary and multi-class int...
Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection and labeling to fea...
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