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Anomalies Detection in Mobile Network Management Data

  • Marco Anisetti
  • Claudio A. Ardagna
  • Valerio Bellandi
  • Elisa Bernardoni
  • Ernesto Damiani
  • Salvatore Reale
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4443)

Abstract

Third generation (3G) mobile networks rely on distributed architectures where Operation and Maintenance Centers handle a large amount of information about network behavior. Such data can be processed to extract higher-level knowledge, useful for network management and optimization. In this paper we apply reduction techniques, such as Principal Component Analysis, to identify orthogonal subspaces representing the more interesting data contributing to overall variance and to split them up in “normal” and “anomalous” subspaces. Patterns within anomalous subspaces allow for early detection of network anomalies, improving mobile networks management and reducing the risk of malfunctioning.

Keywords

Independent Component Analysis Mobile Network Anomaly Detection Independent Component Analysis Network Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marco Anisetti
    • 1
  • Claudio A. Ardagna
    • 1
  • Valerio Bellandi
    • 1
  • Elisa Bernardoni
    • 1
  • Ernesto Damiani
    • 1
  • Salvatore Reale
    • 2
  1. 1.Department of Information Technology, University of Milan, via Bramante, 65 - 26013, Crema (CR)Italy
  2. 2.Siemens S.p.A., Carrier Research & Development Radio Access - Network Management, Via Monfalcone 1, 20092, Cinisello Balsamo (MI)Italy

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