Network Monitoring by Observing Message Codes
F.A. De Almeida
Masters Dissertation, Number: CSHCN MS 1996-2, Year: 1996, Advisor: John S. Baras
The objective of this research is to develop methods to increase the reliability of a hybrid communications network. The main effort will be on early detection, insulation and diagnosis of critical trends in parameters that could lead the network to a potential failure.
The work is initially planned to be divided in five parts: assessment of the raw data (measurements from the network), feature extraction (data preprocessing), classification (data clustering according to the network potential problems), system training (tuning methods) and self-improving (module learning capability).
Raw data analysis: The network database will be accessed to identify the, measurement being performed. The data context and problem definition are, also part of the data analysis process. The problem, the solution to the problem and strategies for solving such problems will be defined according to the network management standards. The data organization, parameters being monitored, data context and its amount are the targets at this level of the research.
Feature extraction: The data is preprocessed in such a way that events in the network are converted into a vector of parameters. The vector that is obtained is called "feature vector". The objective at this level is to transform data into information to be used further in the network monitoring system.