The Research Of Intrusion Detection Based On Improved RBF Neural Network

With the development of the Internet, there are high demands for Internet applications. The network services, such as E-Bank and E-Commerce are becoming the part of life. And Network attacks are increasing. People have realized the importance of network security.IDS is a technology which can protect our information. It can monitor our systems or networks, and find intrusions. The researches and developments of IDS have become important subject to network security. To ride of the shortage of the IDS,we study on the extraction of the features, IDS method which based on neural network, and algorithm optimization and so on.So, the thesis begins its discussion by introducing the concept of IDS, the classification of the IDS and the neural network. And analyze the problems which RBF neural network used in intrusion detection. In this paper, data on the network are choused by performance-based selection, then put forward an improved RBF neural network model for intrusion detection which based on the researches introduce above.By combining Entropy-based fuzzy clustering and neural network,this paper puts forward an improved RBF algorithm based on Entropy-based fuzzy clustering algorithm is applied to intrusion detection. At last we design, analyze, achieve the component of the model.The experimental result is good.