Design and implementation for automated network troubleshooting using data mining

Rozaki, Eleni ORCID: https://orcid.org/0000-0002-1229-4013 2015. Design and implementation for automated network troubleshooting using data mining. International Journal of Data Mining & Knowledge Management Process 5 (3) , pp. 9-27. 10.5121/ijdkp.2015.5302

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Abstract

The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of fault isolation and localisation. The data mining techniques described in this paper are intended to allow a system to be trained to actually learn network fault rules. The results of the tests that were conducted allowed for the conclusion that the rules were highly effective to improve network troubleshooting.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Uncontrolled Keywords:	Decision Trees Fault Diagnosis Data Mining Network Operator Optimisation
Publisher:	AIRCC Publishing Corporation
ISSN:	2230-9608
Date of First Compliant Deposit:	12 September 2016
Date of Acceptance:	10 February 2015
Last Modified:	06 May 2023 02:44
URI:	https://orca.cardiff.ac.uk/id/eprint/94445

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