Detecting flaws and intruders with visual data analysis

Soon Tee Teoh, Kwan-Liu Ma, Soon Felix Wu, T. J. Jankun-Kelly

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Incorporating human perception into the data mining process through interactive visualization can help in better understanding the complex behaviors of computer network systems. This paper illustrates three visualization based methods for detecting network flaws and intrusions. All three applications require appropriate visualization tools and interaction techniques. Because the nature of the data, the task, and the desired knowledge are all different, the visual metaphors used in each of the applications also differ.

Original languageEnglish (US)
Pages (from-to)27-35
Number of pages9
JournalIEEE Computer Graphics and Applications
Volume24
Issue number5
DOIs
StatePublished - Sep 1 2004

Fingerprint

Metaphor
Data Mining
Computer Systems
Visualization
Defects
Computer networks
Data mining

ASJC Scopus subject areas

  • Software
  • Medicine(all)
  • Computer Graphics and Computer-Aided Design

Cite this

Detecting flaws and intruders with visual data analysis. / Teoh, Soon Tee; Ma, Kwan-Liu; Wu, Soon Felix; Jankun-Kelly, T. J.

In: IEEE Computer Graphics and Applications, Vol. 24, No. 5, 01.09.2004, p. 27-35.

Research output: Contribution to journalArticle

Teoh, Soon Tee ; Ma, Kwan-Liu ; Wu, Soon Felix ; Jankun-Kelly, T. J. / Detecting flaws and intruders with visual data analysis. In: IEEE Computer Graphics and Applications. 2004 ; Vol. 24, No. 5. pp. 27-35.
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