关键词: 入侵检测, 数据流, 动态集成, 概念漂移

Abstract: Intrusion data stream is characterized by high speed updating and concept drifting.Static classifier ensemble cannot cope with data distribution in the whole feature space，which results in low detection accuracy.In this paper，a dynamic classifier ensemble based intrusion detection algorithm is presented，which sets the weight of each base classifier dynamically，detecting concept drifting and updating classifier ensemble by interval estimation.Experiment result shows that the proposed algorithm outperforms majority voting and weighted majority voting，two static classifier ensemble methods，and that it has high detection accuracy on real-life intrusion detection dataset.

Key words: intrusion detection, data streams, dynamic ensemble, concept drifting