RT Journal Article
T1 Reasoning and Knowledge Acquisition Framework for 5G Network Analytics
A1 Sotelo Monge, Marco Antonio
A1 Maestre Vidal, Jorge
A1 García Villalba, Luis Javier
AB 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.
PB MDPI
SN 1424-8220
YR 2017
FD 2017-10-21
LK https://hdl.handle.net/20.500.14352/19224
UL https://hdl.handle.net/20.500.14352/19224
LA eng
NO Unión Europea. Horizonte 2020
DS Docta Complutense
RD 10 abr 2025