Diagnosing network faults using bayesian and case-based reasoning techniques

Proper and rapid identification of faults is of premier importance for efficient management of computer networks. Many detection and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid in understanding network problems, perform troubleshooting actions and reduce human intervention under serious situations. This paper introduces a new hybrid approach with the simulation experiments conducted and its results.