Intrusion Detection System Based On Rbf Neural Network

With the development of IT and Internet, the relationship between our lives and Internet are increasingly close. However ,the widespread IT knowledge make the Internet unsafe ,more and more attack tools and ways appear .The passive and static protection like firewall can't satisfy the need of departments for higher safety .Then the IDS (Intrusion Detection System) with active and dynamic protection became the new focus of safe market, it not only concerned by more and more people but used in all kinds of environments.Nowadays, IDS still face many problems .new attack appears everyday, the increase of application on internet add the detection of data amount, all these demand more flexibility and efficiency. The common problems of IDS like false positive and false negative still remain although many technologies like pattern match,expert system etc have applied to IDS. The Neural Network are applied widely because of it's advantages of high self-learning,self-adaptive,robust,parallel computing etc. The problem of applying RBFNN (Radial-Basis Function Neural Network) to IDS will be present in this paper.In order to explain how an IDS is constructed. First, the standards of IDS are introduced and several IDS modals are compared, and then, we introduce the principle of RBF. After that, we construct a DIDS base on RBF, introduce the main function of every module and the method of design in detail.Finally the data of kdd-99 data set were selected and after preprocessing like removing reduplicate data,feature selection etc. We did some emulational experiments to the detection module based on NN ,the result show that IDS base on RBFNN not only improve the rate of detection ,the false positive rate is not too high but also have the ability to detect new intrusion. So the application of RBF will help to the development of IDS.