An anomaly detection system using a GHSOM-1
Esteban J. Palomo, Juan Miguel Ortiz-de-Lazcano-Lobato, Enrique Domínguez, Rafael Marcos Luque‐Baena · 2010
An anomaly detection system based on a hierarchical self-organizing neural network is presented. The proposed neural network reduces the amount of parameters that a user should define prior to the training to a single parameter. This allows the network to perform more autonomously while maintaining a good performance, which is less dependent on the user experience about the application domain. The experimental results show the behavior of the anomaly detection system when it is applied to the KDD Cup 1999 data set.