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April 26, 2026ElectronicsOpen Access

A Robust Botnet Detection Framework Using Homogeneous Radial Basis Function Neural Networks Against Distinct Botnet Types

Lama Awad Sherenaz Al-Haj Baddar Azzam Sleit

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Key Points

This research aims to develop a robust botnet detection framework using homogeneous Radial Basis Function Neural Networks (RBFNN).
Utilized the CTU-13 dataset for flow-level packet length feature extraction.
Implemented a uniform RBFNN architecture across all network components.
Conducted extensive experiments including cross-validation and ROC analysis.
Achieved a classification accuracy of 98.31% with an AUC of 0.997.
Demonstrated a latency of 1.0118 microseconds for real-time processing.
Reported a low false-positive rate of nearly 0.018.

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Implication

Demonstrates high accuracy for botnet detection in network security, suggesting efficient real-time analysis.

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Cite This Study

Awad et al. (2026) studied this question.

synapsesocial.com/papers/69f2f19c1e5f7920c63873f1 https://doi.org/10.3390/electronics15091833

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Robust and Noise-Resilient Botnet Detection Framework Using Heterogeneous Radial Basis Function Neural NetworkOriginal Research · 2026
2Application of representation learning in detecting botnet attacksOriginal Research · 2026
3Detection and Classification of Network Traffic in Bot Network Using Deep Learning2024
4Enhancing botnet attack detection using a hybrid deep learning model combining network flow and DNS query features with explainable AI for cybersecurityOriginal Research · 2026 · 1 citations
5Deep Graph Neural NetworkBased Botnet Detection on IoT NetworksOriginal Research · 2026

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