Anomaly detection in mobile communication networks using the self-organizing map

Issue title: VIII Brazilian Symposium on Neural Networks

Article type: Research Article

Authors: Frota, Rewbenio A. | Barreto, Guilherme A. | Mota, João C.M.

Affiliations: Department of Teleinformatics Engineering, Federal University of Ceará (UFC), CP 6005, CEP 60455-760, Fortaleza, Ceará, Brazil

Note: [] Corresponding author. E-mail: Guilherme@deti.ufc.br

Abstract: Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. In this paper we propose a general procedure for the computation of decision thresholds for anomaly detection in mobile communication networks. The proposed method is based on Kohonen's Self-Organizing Map (SOM) and the computation of nonparametric (i.e. percentile-based) confidence intervals. Through simulations we compare the performance of the proposed and standard SOM-based anomaly detection methods with respect to the false positive rates produced.

Journal: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 5, pp. 493-500, 2007