Network Security Intrusion Detection Technology

With the development of the network technology, the computer network is applied to a lot of fields of the human activity extensively. Therefore the security problem of the network is receiving more and more extensive concerns. In order to find hostility behavior and take action before it do harm to the system and date, the intrusion detection system is born. Intrusion detection has become a strong barrier of the network security. Data mining enables network intrusion detection system, which is a kind of viewpoint that centers on data to improve the ability of the entire systematic detecting, so it can reduce the rate of false-positive and false-negative effectively. Intrusion Detection acts as the effective complement to traditional protection techniques. Data mining, an effective technique in data analyzing, is naturally applied in intrusion detection. Therefore, intrusion detecting system based on data mining is becoming a new researching field.The core of this thesis is the research and application of Detecting system basing on Data mining. First the existing Data-mining technology and Intrusion Detection System technology are analyzed and compared. On the basis of this, I've researched the feasibility and necessity of applying the technique of Data-mining to Intrusion Detection System, I especially researched the association rule algorithm Apriori and clustering rule algorithm K-means. I've also analyzed the disadvantage and lack of the rule and improved both of them. In the last chapter of the paper, the model of Intrusion Detecting system based on Data mining is referred to. And I've designed some modules by using the improved algorithm.In theory, it is feasible to apply the Data-mining based Intrusion Detection System. It's necessary to apply data mining technologies to intrusion detection, and it surely will bring a great renovation.