An auto-mated network management using artificial intelligent techniques

An auto-mated network management has been not only critical but also difficult in the network research area. Among the artificial intelligent techniques, traditional supervised learning techniques are not appropriate for an auto-mated network management and specially to detect temporal changes in network intrusion patterns and characteristics. The reason is that supervised learning needs the manager. Therefore, unsupervised learning techniques such as SOM (self-organizing map) are more appropriate for an auto-mated network management such as configuration, performance and anomaly detection. In this paper, we propose an auto-mated network management based on hierarchical SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features for an auto-mated network management. We experiments our system with KDD Cup 1999 data set. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack.