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

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

Source

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology archive
Volume 18 , Issue 5 (October 2007) table of contents
VIII Brazilian Symposium on Neural Networks

Pages: 493-500

Year of Publication: 2007

ISSN:1064-1246

Authors

Rewbenio A. Frota	Department of Teleinformatics Engineering, Federal University of Ceará (UFC), CP 6005, CEP 60455-760, Fortaleza, Ceará, Brazil
Guilherme A. Barreto	(Correspd. Guilherme@deti.ufc.br) Department of Teleinformatics Engineering, Federal University of Ceará (UFC), CP 6005, CEP 60455-760, Fortaleza, Ceará, Brazil
João C.M. Mota	Department of Teleinformatics Engineering, Federal University of Ceará (UFC), CP 6005, CEP 60455-760, Fortaleza, Ceará, Brazil

Publisher

IOS Press Amsterdam, The Netherlands, The Netherlands

Bibliometrics

Downloads (6 Weeks): n/a, Downloads (12 Months): n/a, Citation Count: 3

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 3

	Mohamed M. Mostafa, Shades of green: A psychographic segmentation of the green consumer in Kuwait using self-organizing maps, Expert Systems with Applications: An International Journal, v.36 n.8, p.11030-11038, October, 2009
	Mohamed M. Mostafa , Nedret Billor, Recognition of Western style musical genres using machine learning techniques, Expert Systems with Applications: An International Journal, v.36 n.8, p.11378-11389, October, 2009
	Mohamed M. Mostafa, Clustering the ecological footprint of nations using Kohonen's self-organizing maps, Expert Systems with Applications: An International Journal, v.37 n.4, p.2747-2755, April, 2010