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Open AccessArticle

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases 9, Ciudad Universitaria, 28040 Madrid, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2017, 17(10), 2405; https://doi.org/10.3390/s17102405
Received: 10 August 2017 / Revised: 17 September 2017 / Accepted: 16 October 2017 / Published: 21 October 2017
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. View Full-Text
Keywords: 5G; analysis; knowledge acquisition; pattern recognition; prediction 5G; analysis; knowledge acquisition; pattern recognition; prediction
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Sotelo Monge, M.A.; Maestre Vidal, J.; García Villalba, L.J. Reasoning and Knowledge Acquisition Framework for 5G Network Analytics. Sensors 2017, 17, 2405.

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