Visual Data Analysis for Detecting Flaws and Intruders in Computer Network Systems
IEEE Computer Graphics and Applications,
24 (5):27–25, IEEE Computer Society
Press 2004.
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 enomrous size of the collected data. Conventional automated methods are largely based on statistical modeling, and some employ machine learning. In this paper, we show interactive visualization as an alternative and effective data exploration method for understanding the complex behaviors of computer network systems. We describe three log-file analysis applications, and demonstrate how the use of our visualization-centered tools can lead to the discovery of flaws and intruders in the network systems.
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Copyright
Copyright 2004, IEEE
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BibTeX Citation
@article{Teoh:2004:VDA,
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 enomrous size of the collected data.
Conventional automated methods are largely based on statistical modeling, and
some employ machine learning. In this paper, we show interactive visualization
as an alternative and effective data exploration method for understanding the
complex behaviors of computer network systems. We describe three log-file
analysis applications, and demonstrate how the use of our
visualization-centered tools can lead to the discovery of flaws and intruders
in the network systems.},
Author = {Soon Tee Teoh AND Kwan-Liu Ma AND Soon Felix Wu AND T.J.
Jankun-Kelly},
Doi = {10.1109/MCG.2004.26},
Issn = {0272-1716},
Journal = {IEEE Computer Graphics and Applications},
Keywords = {information visualization, intrusion detection, visual data
mining, network visualization, internet routing stability},
Month = {September/October},
Number = {5},
Pages = {27--35},
Publisher = {IEEE Computer Society Press},
Title = {Visual Data Analysis for Detecting Flaws and Intruders in Computer
Network Systems},
Url = {http://doi.ieeecomputersociety.org/10.1109/MCG.2004.26},
Volume = {24},
Year = {2004}}
Contact
Dr. T.J. Jankun-Kelly [tjk@cse.msstate.edu], Department of
Computer Science and Engineering, Mississippi State University.
