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Detecting flaws and intruders with visual data analysis

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4 Author(s)
Soon Tee Teoh ; California Univ., Davis, CA, USA ; Kwan-Liu Ma ; Wu, S.F. ; Jankun-Kelly, T.J.

The task of sifting through large amounts of data to find useful information spawned the field of data mining. Most data mining approaches are based on machine-learning techniques, numerical analysis, or statistical modeling. They use human interaction and visualization only minimally. Such automatic methods can miss some important features of the data. Incorporating human perception into the data mining process through interactive visualization can help us better understand the complex behaviors of computer network systems. This article describes visual-analytics-based solutions and outlines a visual exploration process for log analysis. Three log-file analysis applications demonstrate our approach's effectiveness in discovering flaws and intruders in network systems.

Published in:

Computer Graphics and Applications, IEEE  (Volume:24 ,  Issue: 5 )