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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17710594 |
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| _version_ | 1866901668614897664 |
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| author | Sharkhan, Aruzhan Myrzabayeva, Manshuk Anuar, Maksat |
| author_facet | Sharkhan, Aruzhan Myrzabayeva, Manshuk Anuar, Maksat |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17710594 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Anomaly Detection in Network Traffic Using Machine Learning Sharkhan, Aruzhan Myrzabayeva, Manshuk Anuar, Maksat <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> |
| title | Anomaly Detection in Network Traffic Using Machine Learning |
| url | https://doi.org/10.5281/zenodo.17710594 |