Anomalies detection in mobile network management data

Authors:
Marco Anisetti;Claudio A. Ardagna;Valerio Bellandi;Elisa Bernardoni;Ernesto Damiani;Salvatore Reale
Affiliations:
Department of Information Technology, University of Milan, Crema, Italy;Department of Information Technology, University of Milan, Crema, Italy;Department of Information Technology, University of Milan, Crema, Italy;Department of Information Technology, University of Milan, Crema, Italy;Department of Information Technology, University of Milan, Crema, Italy;Siemens S.p.A. Carrier Research & Development Radio Access - Network Management, Italy
Venue:
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Year:
2007

Citing 3
Cited 0

Self-similarity in World Wide Web traffic: evidence and possible causes

IEEE/ACM Transactions on Networking (TON)
Adjustment Learning and Relevant Component Analysis

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Structural analysis of network traffic flows

Proceedings of the joint international conference on Measurement and modeling of computer systems

Quantified Score

Hi-index	0.00

Visualization

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.