Learning Method in Intrusion Detection System Based on Neural Network
Haipeng Chen · Journal of Jilin University · 2008
To solve the problems in competitive layer,an added and deleted competitive neural network is proposed.It's unsupervised learning method is based on the Hebbian postulate and a new competitive learning method is adopted.The main idea of learning is that the similarity level decides the rewarded and penalized rate.To overcome the dead units problems it adds new neuron when it is necessary to constitute a new cluster.After learning,another important task is to detect whether there are wrong clusters,if it finds one,it will delete the cluster and combine it's elements with the cluster which is the most similar cluster to the wrong cluster,and the result of clustering is more accurate