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A performance management system for telecommunication network using AI techniques

Zhang, S, Zhang, R and Jiang, J (2008) A performance management system for telecommunication network using AI techniques In: DepCoS - RELCOMEX ’08, 2008-06-26 - 2008-06-28, Szklarska Poreba, Poland.

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Abstract

Anomaly detection has become more and more difficult for telecommunication network due to the various trends of networking technologies and the growing number of unauthorized activities in the performance data. This paper builds up a performance management system based on the one-class-support vector machine (OCSVM) and k-means clustering algorithm, which achieves not only the automatic detection of network anomalies but also the clustering of the anomalies with different levels. The OCSVM detects the anomalies by solving an optimal problem to separate the nominal data from the anomalies; these detected anomalies are then classified into minor, medium and severe levels using k-means clustering. The real telecommunication performance data are employed in this paper for the investigation, and the numerical results demonstrate the promising performance of this system.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Zhang, SUNSPECIFIEDUNSPECIFIED
Zhang, RUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 2008
Identification Number : https://doi.org/10.1109/DepCoS-RELCOMEX.2008.32
Contributors :
ContributionNameEmailORCID
publisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:24
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835197

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