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Detecting Flaws and Intruders with Visual Data Analysis
September/October 2004 (vol. 24 no. 5)
pp. 27-35
Soon Tee Teoh, University of California, Davis
Kwan-Liu Ma, University of California, Davis
Soon Felix Wu, University of California, Davis
T.J. Jankun-Kelly, Mississippi State University
To ensure the normal operation of a large computer network system, the common practice is to constantly collect system logs and analyze the network activities for detecting anomalies. Most of the analysis methods in use today are highly automated due to the enormous size of the collected data. Conventional automated methods are largely based on statistical modeling, and some employ machine learning. This article presents interactive visualization as an alternative and effective data exploration method for understanding the complex behaviors of computer network systems. It describes three log-file analysis applications, and demonstrates how the use of the authors' visualization-centered tools can lead to the discovery of flaws and intruders in the network systems.

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Index Terms:
information visualization, intrusion detection, visual data mining, network visualization, Internet routing stability
Soon Tee Teoh, Kwan-Liu Ma, Soon Felix Wu, T.J. Jankun-Kelly, "Detecting Flaws and Intruders with Visual Data Analysis," IEEE Computer Graphics and Applications, vol. 24, no. 5, pp. 27-35, Sept.-Oct. 2004, doi:10.1109/MCG.2004.26
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