A supervised clustering algorithm for computer intrusion detection

Authors:
Xiangyang Li;Nong Ye
Affiliations:
University of Michigan—Dearborn, Department of Industrial and Manufacturing Systems Engineering, 48128, Dearborn, MI, USA;Arizona State University, Department of Industrial Engineering, 48128, Tempe, AZ, USA
Venue:
Knowledge and Information Systems
Year:
2005

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Abstract

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.