Detecting Flaws and Intruders with Visual Data Analysis

  • Authors:
  • Soon Tee Teoh;Kwan-Liu Ma;Soon Felix Wu;T. J. Jankun-Kelly

  • Affiliations:
  • University of California, Davis;University of California, Davis;University of California, Davis;Mississippi State University

  • Venue:
  • IEEE Computer Graphics and Applications
  • Year:
  • 2004

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Abstract

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.