Intrusion Detection Model Based On Fuzzy 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 research and development of IDS have become important subject about network security. To ride of the shortage of the ID, we study on the extraction of the features, ID method based on fuzzy 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, fuzzy neural network and the genetic algorithm. Then we study on how to analyze the data effectively and report outcome in this field after collecting the primitive data. By virtue of the above study, a kind of ID module based on fuzzy neural network that has mighty classification ability is proposed. And we adopt the research outcome in aspect of intrusion classification and intrusion detection normalization. The method that use both Fuzzy BP algorithm and genetic algorithm is proposed , and this method has overcome the limitation of the BP and genetic algorithm. At last we design, analyze, achieve the component of the FNNIDM. The experimental result is good.