Intrusion detection system is an important component of the computer and information security framework. Its main goal is to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive, and its main challenge is to efficiently detect intrusion detection behaviors for reducing false positive rate and false negative rate. In view of the disadvantages of the existing intrusion detection methods, fuzzy c-means(FCM) clustering method is used to analyze intrusion detection data in order to detect anomaly network behavior patterns. Experimental results on the CUP99 data set data show that this method can not only feasible but also improve the accuracy and efficiency.