Intrusion Detection System Based On Neural Network Technology, Network And Implementation

Intrusion Detection System (IDS) is the research hotspot in the field of Network Security, which plays an important role in safeguarding Network Security. However, there exist the management of Rule Sets, the difficulty in establishing Statistical Model, and the high false positive rate and false negative rate in traditional Intrusion Detection Systems. All of these restrict the usability of Intrusion Detection Systems.Under this background, we bring forward the idea of applying Neural Networks in Network Intrusion Detection Systems (NIDS).One reason for doing so is that the problem to be solved by NIDS is to differentiate normality or abnormality from the network stream, in this sense, Intrusion Detection may be understood as Pattern Recognition . The other reason is that the application of Neural Network in Pattern Recognition has been proved to be very effective. By virtue of the self-learned, associational memory and fuzzy computing of the Neural Network, we hope to solve some problems of the Intrusion Detection Systems.Based on this idea, we design and develop a Neural Network-Based Network Intrusion Detection System prototype. The results of testing the prototype indicate that the idea of applying Neural Network in NIDS is feasible. We have also found some problems during the study, which we will try to research and solve in the future study, and we will continue trying the more effective way of applying Neural Network in the Intrusion Detection Systems.