Method for Getting Inter-Independent Features Used to Intrusion Detection System in Controllable and Trusted Networks

In order to improve the reality, useful and whole performance of network intrusion detection system (IDS), the solution to get independent features used to (or evidence) IDS is presented in the paper. The approach of auto-recognition of inter-relativity of the features is developed, which is classification method for features. The features picked up by the method are used as the input of back propagate neural network (BPNN). The features are inter-independent, or weak relative. On the base of the chosen features, an IDS is built. Tests show that the approach and IDS developed in the article is useful and available. It is conclusion that a set of inter-independent features should be provided for IDS. The inter-relative degree of features can be required with the help of the method developed in the article.