Number Of Applications Of Neural Networks And Data Fusion Methods In Intrusion Detection

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, it's necessary to employ active security resolution to protect network resources. Although firewall and other existing network security mechanisms have been widely used, but with the unending development of network technology, new types of network intrusions and attacks present continuously, and they became more and more complex, so it's almost impossible to avoid intrusions absolutely. As a new network security technology, intrusion detection technology has become the major concern of network security researching field.In this paper, a pattern recognition approach to network intrusion detection based on neural network and data fusion is proposed. This paper firstly studies researching achievement of both intrusion detection system and data fusion, then brings out a method: uses neural network to detect intrusion, then uses D-S evidence theory method to enforce data fusion upon detection result to get higher detection precision.The work including:(1) Pretreatment of test data and classed based on the feature attribute of network connect.(2) Train neural network and get primary result data of intrusion detection.(3) In the D-S evidence model, Fusion the primary result data of neural network to get the finally conclusions and enforce high detection precision.At last, make a summary of shortage of research and farther work and discuss the way and the development of the unborn intrusion detection technology.