A supervised clustering algorithm for computer intrusion detection

Xiangyang Li, Nong Ye

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)498-509
Number of pages12
JournalKnowledge and Information Systems
Issue number4
StatePublished - Nov 2005


  • Classification
  • Clustering
  • Intrusion detection

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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