Sotelo Monge, Marco AntonioMaestre Vidal, JorgeGarcía Villalba, Luis Javier2023-06-182023-06-182017-10-211424-822010.3390/s17102405https://hdl.handle.net/20.500.14352/19224Autonomic 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.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/journal articlehttps://doi.org/10.3390/s17102405https://www.mdpi.com/1424-8220/17/10/2405open access5Ganalysisknowledge acquisitionpattern recognitionpredictionInteligencia artificial (Informática)Internet (Informática)Redes1203.04 Inteligencia Artificial3325 Tecnología de las Telecomunicaciones