Abstract/Description
The efficient and effective monitoring of networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this research is to present a monitoring scheme for system networks based on the use of rules and algorithms to upgrade fault detection and handling. The goal is to have optimization 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 localization. Techniques described in this research 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. The system was designed using VB.Net as frontend and Microsoft SQ Server was used as the system backend for regular backup.
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