Abstract: According to network intrusion detection system,data mining are frequently faced with unbalanced data sets,and the mining of minority classes from unbalanced database is research focus. Aim at mining of minority classes from unbalanced database,this paper proposes an association rules mining from unbalanced database called ARUD,the proposed algorithm through based on pairwise item condition probability to calculate the cogency of find all association rules,ARUD only one passes through the file.In this paper,four typical data sets from the UCI,compared with Apriori and CFP-Growth,ARUD has the superiority of the approach for classifying minority classes in unbalanced data sets.In addition,ARUD is consistently faster and consumes less memory space.