Automated Network Fault Management

In this thesis, an approach involving the use of a hybrid system involving both neural networks and expert systems for performing automated network fault management is investigated. Data networks using the X.25 protocol are considered. A minimum cost routing scheme is used for re-routing future calls given the occurrence of a fault. A method for partitioning the data (obtained from the X.25 network) between the neural network and the expert system is suggested. Radial basis function networks are used as the neural network architecture for performing fault classification using performance data. Queries are provided for the expert system to determine the type of fault that occurred using the results of the neural network, together with alarms, SNMP traps, and X.25 SNMP statistics.