Reasoning and Knowledge Acquisition Framework for 5G Network Analytics



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Sotelo Monge, Marco Antonio and Maestre Vidal, Jorge and García Villalba, Luis Javier (2017) Reasoning and Knowledge Acquisition Framework for 5G Network Analytics. Sensors, 17 (10). p. 2405. ISSN 1424-8220

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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.

Item Type:Article
Uncontrolled Keywords:5G; analysis; knowledge acquisition; pattern recognition; prediction
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science > Internet
Sciences > Computer science > Networks
ID Code:67713
Deposited On:08 Sep 2021 15:12
Last Modified:08 Sep 2021 15:17

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