Intrusion detection system is a newly emerging and promising security measure. Data mining methods have been used to build automatic intrusion detection systems based on anomaly detection. The goal is to characterize the normal system activities with a profile by applying mining algorithms to audit data so that abnormal intrusive activities can be detected by comparing the current activities with the profile. This paper provides a new Intrusion Detection method based on data mining technology and combines fuzzy logic with apriori mining method. By grouping the quantitative attributes in network traffic according to fuzzy set, and by using genetic algorithm to construct the membership functions that state the fuzzy set, the existing "sharp boundary" problem can be avoided if the classic set theory is adopted. The experiment result shows that this combining fuzzy logic data mining method is an effective anomaly detection way.