Network Weight Updating Method for Intrusion Detection Using Artificial Neural Networks
Application of Artificial Neural Networks (ANN) to intrusion detection has been considered in this paper. Experimental data were collected using KDD database. Using the data collected, the training patterns and test patterns are obtained. An ANN has been used to train the data offline. The weight updating algorithms developed for the ANN are based on the back propagation algorithms, echo state neural network and the functional update method. The method of presenting the patterns to the input layer of the network has been analyzed. The different methods of presenting the input patterns, such as reducing the dimension of the input patterns by a transformation and preprocessing of the input patterns for non-linear classifiers have been investigated.