An analysis of intrusion detection system using back propagation neural network

In recent years, internet and computers have been utilized by many people all over the world in several fields. In order to come up with efficiency and up to date issues, most organizations rest their applications and service items on internet. On the other hand, network intrusion and information safety problems are ramifications of using internet. The growing network intrusions have put companies and organizations at a much greater risk of loss. In this paper, propose a new learning methodology towards developing a novel intrusion detection system (IDS) by back propagation neural networks (BPN). The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. There are several techniques which exist at present to provide more security to the network, but most of these techniques are static. Test the proposed method by a benchmark intrusion dataset to verify its feasibility and effectiveness. Results show that choosing good attributes and samples will not only have impact on the performance, but also on the overall execution efficiency. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors analyze, model and understand the complex attack behavior of electronic crime.