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.
This study investigates the development and evaluation of a hybrid Intrusion Detection System (IDS) that integrates rule-based detection with machine learning (ML) techniques to address the limitations of standalone approaches in modern cybersecurity environments. Guided by a Design Science Research framework, the stud...
The increasing complexity of cyber threats has strengthened the need for adaptive network intrusion detection systems (IDS). This systematic literature review (SLR) synthesizes nine peer-reviewed studies published from 2024 to 2026 on deep learning (DL)-based network anomaly detection. The review follows a PRISMA 2020-...
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...
Intrusion Detection Systems (IDS) are essential for protecting modern networks against unauthorized access and evolving cyber threats. A persistent challenge in IDS design is the high dimensionality of network traffic data, which complicates the identification of the most relevant features for effective detection. This...
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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