Research Of GA-based Distributed Intrusion Detection System

Because of the increasing dependence, which corporations, government agencies and other organizations have on their computer networks, the importance of protecting these systems from attacks seems critical. With the growing number of computer systems being connected to their networks, Intrusion Detection System (IDS) is clearly necessary.Since Intrusion Classification is the foundation of the analysis, design and implementation of the IDS, it is certainly very popular. First above all, this thesis divides the network into three factors:Data, Relation and Ability.Furthermore, grounded on the model the thesis provides a classification based on DRA.Secondly, this thesis do some research in studying intrusion detection technology across-the-board, including introducing the classification of the IDS, analyzing various detection methods and summerizing the menaces that current IDS will face theoretically.Afterwards, given the fact that Genetic Algorithm predominates in the non-linear problems, this paper brings forward the frame of GA-based Distributed IDS.And the adventages of the frame are as follows: pretreatment and fusion of the raw data from the distributed sensors, bearing the ability of self-learning, "distributed detection and concentrative analysis".In the end, Genetic Algorithm is brought out and introduced comprehensively. Moreover.the paper puts emphasis on the experimental flow that Genetic Algorithm is used to train data in detail and the results which are supplied in the laboratory.