Research Of Intrusion Detection Method Based On Data Mining

With a wide range of applications and the rapid development of the network, network security becomes particularly important. How to quickly and efficiently protect the network security systems and resources, has become urgent research topics in the field of network security. For existing traditional intrusion detection systems, most of them there is a high rate of false positives, scalability is poor, detection dysfunction.This paper studies the intrusion detection system based on data mining methods. From the data processing point of view, the data mining and abnormality detection combination First, a detailed introduction to the basic concepts and principles of intrusion detection systems, the advantages and disadvantages of the traditional detection techniques; Describes the structure of data mining and related algorithms; Then proposed CBUID combined with the k-means algorithm. Create a new detection model, Experiments show that the system has a higher detection rate and lower false detection rate, to meet the needs of the intrusion detection system.