Design and implementation for automated network troubleshooting using data mining

TL;DR

This paper presents a data mining-based monitoring scheme for mobile networks that enhances fault detection, isolation, and localization through rule-based and Bayesian classifiers, leading to improved troubleshooting efficiency.

Contribution

It introduces a novel mobile network monitoring framework utilizing rule-based and Bayesian data mining classifiers for fault detection and localization.

Findings

Rules significantly improved anomaly detection accuracy

Bayesian classifiers effectively localized faults

System learned network fault rules with high effectiveness

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.