Research Of A Neural Network Based Intrusion Detection System Model

Recently, as the development of the technology of the Internet, the security problem becomes a thread of the network field. One of the threads is intrusion which is to get or destroy the important information of users. In some case it can paralyze the whole network. And Intrusion Detection System is an advanced technology for network security and is becoming one of hotspots in the network security field. It has taken great effects in safeguarding the security of our network.However, the traditional Intrusion Detecting System has some disadvantages such as difficulty in rule base administration and statistics model building, as well as high false negative and false positive rates. All of the weak points above have limited the effects of the Intrusion Detecting Systems. In order to improve the disadvantages of our Intrusion Detecting Systems, we introduce a Neural Network based Intrusion Detecting model. The principle of the Intrusion Detection is to get the information of the network traffics and then analyze them to determine whether the system is under attack. So we can look the intrusion detecting as a Pattern Recognition problem. Accordingly, Neural Networks is a mathematical model that has the characteristics of self-study, self-organization and self-adapting and has been widely use in the Pattern Recognition field. Therefore, we can use this technology to detect the intrusion in the network.Then we implement a Neural Network based Intrusion Detecting Model based on the theory above and improve the BP algorithm to increase the performance of our system model. Then we test the model use real data in the network and acquire good results.