To analyze tens of thousands of alarms triggered by the intrusion detections systems (IDS) a day has been very time-consuming, requiring human administrators to stay alert for all time. But most of the alarms triggered by the IDS prove to be the false positives. If alarms could be correctly classified into the false positive and the false negative, then we could alleviate most of the burden of human administrators and manage the IDS far more efficiently. Therefore, we present a new approach based on attribute-oriented induction (AOI) to classify alarms into the false positive and the false negative. The experimental results show the proposed approach performs very well.