Using incremental learning method for adaptive network intrusion detection

This paper proposes an adaptive on-line intrusion detection model based on incremental rule learning. This model can make self-learning over the ever-emerged new network behavior examples and dynamically modify behavior profile of the model, which overcomes the disadvantage that the traditional static detecting model must relearn over all the old and new examples, even can´t relearn because of limited memory size. The experiment results validate the feasibility and effectivity of the presented adaptive intrusion detection model.