As active defense technology, IDS (Intrusion Detection System) compensates the defects of traditional protection mechanism system, but in the face of rapid updated network configurations, the drastic increase of network traffic and do many new attach methods, traditional IDS has some limitations. The combination of data mining and intrusion detecting enables the intrusion detection system to have the ability of self-study and to have a better dealing with a vast amount of data as well as to enhance the detecting ability and lighten security managers' work. The combination is practical and conforms to the trend of the development of intrusion detection system.This paper studies on the intrusion detection based on data mining, analyses the intrusion detection technology, and concludes its developing direction. The main works of this paper are summarized as follows:1. A modified fuzzy C-means algorithm is proposed in order to solve the question of sharp border effecting problem in the intrusion detection.2. A learning algorithm, called FORBF, is used in intrusion detection based on modified FCM and orthogonal least squares (OLS) with the aim at improving the classification accuracy of the RBFNN.The result of emulation examinations on KDD 1999 indicates the system indicates the detecting speed, increase the efficiency of intrusion, and can detect variety of unknown intrusions.