Publication: Diseño e implementación de una fuente de datos tipo SNORT basada en la arquitectura SELFNET
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2017
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Abstract
Este trabajo está basado en el proyecto SELFNET y cuenta con el apoyo del Programa Horizonte 2020 de la Comisión Europea con número de referencia H2020-ICT-2014-2/671672.
El proyecto SELFNET diseña e implementa un marco de gestión autónomo de la red para lograr capacidades de auto-organización en la gestión de infraestructuras de red. Mediante la detección y mitigación automática de problemas de red, los operadores pueden reducir significativamente los costes operacionales.
SELFNET integra en su arquitectura tecnologías innovadoras tales como las Redes Definidas por Software (SDN), la Virtualización de Funciones de Red (NFV), las Redes Auto-Organizadas (SON), Computación en la Nube, Inteligencia Artificial, Calidad de Experiencia (QoE), etc., para proporcionar un nuevo marco inteligente en la gestión de redes.
El presente trabajo forma parte de la arquitectura de monitorización de SELFNET, diseñando e implementando una fuente de datos capaz de recibir notificaciones de un sensor tipo SNORT. Asimismo, la información recibida es adaptada según un modelo general de métricas. Finalmente, las métricas son enviadas a capas superiores de agregación y correlación. Los resultados obtenidos demuestran la eficiencia del diseño realizado.
This work is based on the European SELFNET project and is supported by the Horizon 2020 Program of the European Commission under the reference number H2020-ICT-2014-2/671672. The SELFNET project designs and implements an autonomous network management framework to achieve self-organizing capabilities in the management of network infrastructures. By automatically detecting and mitigating network problems, operators can significantly reduce operational costs. SELFNET integrates in its architecture innovative technologies such as Software Defined Networks (SDN), Network Function Virtualization (NFV), Self-Organizing Networks (SON), cloud computing, Artificial Intelligence, quality of experience (QoE), to provide a new intelligent framework in network management. The present work is part of the SELFNET Monitoring architecture. In which, a data source capable of receiving notifications of an SNORT type sensor is designed and implemented. Likewise, the information received is adapted according to a general model of metrics. Finally, metrics are sent to higher layers of aggregation and correlation. The implementation and results demonstrate the efficiency of the present design.
This work is based on the European SELFNET project and is supported by the Horizon 2020 Program of the European Commission under the reference number H2020-ICT-2014-2/671672. The SELFNET project designs and implements an autonomous network management framework to achieve self-organizing capabilities in the management of network infrastructures. By automatically detecting and mitigating network problems, operators can significantly reduce operational costs. SELFNET integrates in its architecture innovative technologies such as Software Defined Networks (SDN), Network Function Virtualization (NFV), Self-Organizing Networks (SON), cloud computing, Artificial Intelligence, quality of experience (QoE), to provide a new intelligent framework in network management. The present work is part of the SELFNET Monitoring architecture. In which, a data source capable of receiving notifications of an SNORT type sensor is designed and implemented. Likewise, the information received is adapted according to a general model of metrics. Finally, metrics are sent to higher layers of aggregation and correlation. The implementation and results demonstrate the efficiency of the present design.
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Universidad Complutense, Facultad de Informática, curso 2016/2017)