Machine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation Article Swipe
Pilar Schummer
,
Alberto del Río
,
Javier Serrano
,
David Jiménez
,
Guillermo Sánchez
,
A. Llorente
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ai5040143
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ai5040143
Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies. SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ai5040143
- https://www.mdpi.com/2673-2688/5/4/143/pdf?version=1734433888
- OA Status
- gold
- Cited By
- 11
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405501328
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405501328Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ai5040143Digital Object Identifier
- Title
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Machine Learning-Based Network Anomaly Detection: Design, Implementation, and EvaluationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-17Full publication date if available
- Authors
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Pilar Schummer, Alberto del Río, Javier Serrano, David Jiménez, Guillermo Sánchez, A. LlorenteList of authors in order
- Landing page
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https://doi.org/10.3390/ai5040143Publisher landing page
- PDF URL
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https://www.mdpi.com/2673-2688/5/4/143/pdf?version=1734433888Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-2688/5/4/143/pdf?version=1734433888Direct OA link when available
- Concepts
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Anomaly detection, Interpretability, Computer science, Artificial intelligence, Anomaly (physics), Cluster analysis, Machine learning, Outlier, Data mining, Scalability, Unsupervised learning, Software deployment, Condensed matter physics, Operating system, Physics, DatabaseTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 11Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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