• español
    • English
  • Login
  • español 
    • español
    • English
  • Tipos de Publicaciones
    • bookbook partconference objectdoctoral thesisjournal articlemagazinemaster thesispatenttechnical documentationtechnical report
Ver ítem 
  •   IMDEA Networks Principal
  • Ver ítem
  •   IMDEA Networks Principal
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.

Explainable Machine Learning for Performance Anomaly Detection and Classification in Mobile Networks

Compartir
Ficheros
Netpredict3_Cleaning_Matters__Journal_version.pdf (12.64Mb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1655
Metadatos
Mostrar el registro completo del ítem
Autor(es)
Ramirez, Juan Marcos; Díez Muñoz, Fernando; Rojo, Pablo; Mancuso, Vincenzo; Fernández Anta, Antonio
Fecha
2023-02-15
Resumen
Mobile communication providers continuously collect many parameters, statistics, and key performance indicators (KPIs) with the goal of identifying operation scenarios that can affect the quality of Internet-based services. In this regard, anomaly detection and classification in mobile networks have become challenging tasks due to both the huge number of involved variables and the unknown distributions exhibited by input features. This paper introduces an unsupervised methodology based on both a data-cleaning strategy and explainable machine learning models to detect and classify performance anomalies in mobile networks. Specifically, this methodology dubbed explainable machine learning for anomaly detection and classification (XMLAD) aims at identifying features and operation scenarios characterizing performance anomalies without resorting to parameter tuning. To this end, this approach includes a data cleaning stage that extracts and removes outliers from experiments and features to train the anomaly detection engine with the cleanest possible dataset. Moreover, the methodology considers the differences between discretized values of the target KPI and labels predicted by the anomaly detection engine to build the anomaly classification engine which identifies features and thresholds that could cause performance anomalies. The proposed methodology incorporates two decision tree classifiers to build explainable models of anomaly detection and classification engines whose decision structures recognize features and thresholds describing both normal behaviors and performance anomalies. We evaluate the XMLAD methodology on real datasets captured by operational tests in commercial networks. In addition, we present a testbed that generates synthetic data using a known TCP throughput model to assess the accuracy of the proposed approach.
Compartir
Ficheros
Netpredict3_Cleaning_Matters__Journal_version.pdf (12.64Mb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1655
Metadatos
Mostrar el registro completo del ítem

Listar

Todo IMDEA NetworksPor fecha de publicaciónAutoresTítulosPalabras claveTipos de contenido

Mi cuenta

Acceder

Estadísticas

Ver Estadísticas de uso

Difusión

emailContacto person Directorio wifi Eduroam rss_feed Noticias
Iniciativa IMDEA Sobre IMDEA Networks Organización Memorias anuales Transparencia
Síguenos en:
Comunidad de Madrid

UNIÓN EUROPEA

Fondo Social Europeo

UNIÓN EUROPEA

Fondo Europeo de Desarrollo Regional

UNIÓN EUROPEA

Fondos Estructurales y de Inversión Europeos

© 2021 IMDEA Networks. | Declaración de accesibilidad | Política de Privacidad | Aviso legal | Política de Cookies - Valoramos su privacidad: ¡este sitio no utiliza cookies!