分类器动态集成的入侵数据流检测算法  

Data stream intrusion detection algorithm based on dynamic classifier ensemble

作　　者：迟茜[1] 赵楠 

机构地区：[1]西北农林科技大学机电学院,西安712100 [2]航天第二研究院25所,北京100871

出　　处：《计算机工程与应用》2009年第29期111-113,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.70571065)~~

摘　　要：入侵数据流具有快速更新以及概念漂移的特点,静态集成分类器无法及时反映整个空间的数据分布,入侵检测正确率不高,对此,文中提出了一种单分类器动态集成的入侵检测方法,该方法动态分配各分类器权值并用区间估计检查概念漂移并更新分类器。实验结果表明,在处理超平面构造的数据流上,分类效果优于多数投票、加权投票两种静态分类方法,在真实入侵实数据集上有高检测率。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.ln 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.

关 键 词：入侵检测 数据流 动态集成 概念漂移 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]