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| Main Authors: | , , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17710594 |
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Table of Contents:
- <p>This study explores the application of machine learning techniques for detecting anomalies and cyberattacks in network traffic. A comparative analysis was conducted between a traditional rule-based intrusion detection method and a machine learning ensemble model. Using a full 2^3 factorial experimental design, the influence of three key factors—detector type, the use of a Threat Intelligence module, and network traffic load—on the F1-score was evaluated. The results show that the machine learning ensemble significantly improves detection accuracy (approximately 30% increase), while integrating external Threat Intelligence provides an additional performance gain (~7%). High traffic load, however, reduces detection quality by around 7%. Regression modelling and graphical interpretation confirmed that the detector type is the most influential factor. The findings demonstrate the effectiveness of machine learning-based approaches in intrusion detection systems and offer practical recommendations for enhancing cybersecurity solutions.</p>