The Research Of Intrusion Detection System Based On Data Mining

With the increase of informatization level and enhancement of dependence on computer networks for human society, Computer network security has aroused extensive attention. Intrusion Detection is a security technology to detect the intrusion through monitoring the target system in runtime. Now it has become a hot research in a field of network security.Traditional IDS has some limitations: poor adaptability, lack of extensibility, and inability to detect novel attacks. Based on the thorough research of intrusion detection technology and data mining technology, The Research of Intrusion Detection System Based on Data mining is put forward in this dissertation. The main work were as follows:First, researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm. The algorithm is proved to have good performance in real-time detect with some experiments.Second, Analyzed the association rule mining and the sequential pattern mining algorithms. FP-growth and Prefixspan algorithms were used in network connectivity records. Compared with traditional methods, and improved the efficiency of the system.Third, based on the research on the intrusion detection technology and intrusion method in common use, a solution of runtime Intrusion Detection System based on data mining is proposed in the dissertation. The model has self-adaptability and strong extendable feature, and realizes low error detecting rare and misinformation rate. Thus, it achieves the goal of improving intrusion detection quality, and has widely application value...