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Intrusion detection systems (IDS) protect computer systems by providing alerts which might be caused by malicious attacks. Learning methods were introduced into intrusion detection to automatically improve the performance using history data. Yet high quality data requires heavy labor of experts or expensive monitoring process. Meanwhile, IDS should minimize a nonuniform cost of the misclassification. In the paper, we aim to reduce the burden of labeling data for constructing the intrusion detection classifier with the least misclassification cost. We proposed a novel active cost-sensitive learning method for intrusion detection using the technologies of active learning and cost-sensitive learning. The proposed method uses a popular cost-sensitive learning method Metacost as the base classifier and a sampling criterion of the largest misclassification cost. The results of the experiments on intrusion detection datasets of KDDCUP 99 show that the proposed method is effective.