A supervised clustering algorithm for computer intrusion detection

被引：0

作者：

Xiangyang Li

Nong Ye

机构：

[1] University of Michigan—Dearborn,Department of Industrial and Manufacturing Systems Engineering

[2] Arizona State University,Department of Industrial Engineering

来源：

Knowledge and Information Systems | 2005年 / 8卷

关键词：

Classification; Clustering; Intrusion detection;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We previously developed a clustering and classification algorithm—supervised (CCAS) to learn patterns of normal and intrusive activities and to classify observed system activities. Here we further enhance the robustness of CCAS to the presentation order of training data and the noises in training data. This robust CCAS adds data redistribution, a supervised hierarchical grouping of clusters and removal of outliers as the postprocessing steps.

引用

页码：498 / 509

页数：11

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