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Issue No.02 - March/April (2006 vol.26)
pp: 72-80
John R. Goodall , University of Maryland, Baltimore County
Wayne G. Lutters , University of Maryland, Baltimore County
Penny Rheingans , University of Maryland, Baltimore County
Anita Komlodi , University of Maryland, Baltimore County
ABSTRACT
Intrusion detection analysis requires understanding the context of an event, usually discovered by examining packet-level detail. When analysts attempt to construct the big picture of a security event, they must move between high-level representations and these low-level details. This continual shifting places a substantial cognitive burden on the analyst, who must mentally store and transfer information between these levels of analysis. This article presents an information visualization tool, the time-based network traffic visualizer (TNV), which reduces this burden. TNV augments the available support for discovering and analyzing anomalous or malicious network activity. The system is grounded in an understanding of the work practices of intrusion detection analysts, particularly foregrounding the overarching importance in the analysis task of integrating contextual information into an understanding of the event under investigation. TNV provides low-level, textual data and multiple, linked visualizations that enable analysts to simultaneously examine packet-level detail within the larger context of activity.
INDEX TERMS
information visualization, user-centered design, network analysis, visualization for computer security
CITATION
John R. Goodall, Wayne G. Lutters, Penny Rheingans, Anita Komlodi, "Focusing on Context in Network Traffic Analysis", IEEE Computer Graphics and Applications, vol.26, no. 2, pp. 72-80, March/April 2006, doi:10.1109/MCG.2006.31
REFERENCES
1. J.R. Goodall, "User Requirements and Design of a Visualization for Intrusion Detection Analysis," Proc. IEEE Workshop Information Assurance and Security (IAW), IEEE Press, 2005, pp. 394-401.
2. K. Julisch, "Clustering Intrusion Detection Alarms to Support Root Cause Analysis," ACM Trans. Information and System Security, vol. 6, no. 4, 2003, pp. 443-471.
3. . Mackinlay, G.G. Robertson, and S.K. Card, "The Perspective Wall: Detail and Context Smoothly Integrated," Proc. ACM Conf. Human Factors in Computing Systems (CHI), ACM Press, 1991, pp. 173-179.
4. A. Inselberg, "The Plane with Parallel Coordinates," The Visual Computer, vol. 1, 1985, pp. 69-91.
5. J. McHugh, "Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Off-Line Intrusion Detection System Evaluation as Performed by Lincoln Laboratory," ACM Trans. Information and System Security, vol. 3, no. 4, 2000, pp. 262-294.
6. J.R. Goodall, W.G. Lutters, and A. Komlodi, "I Know My Network: Collaboration and Expertise in Intrusion Detection," Proc. ACM Conf. Computer-Supported Cooperative Work (CSCW), ACM Press, 2004, pp. 342-345.
7. J.R. Goodall et al., "A User-Centered Approach to Visualizing Network Traffic for Intrusion Detection," Extended Abstracts ACM Conf. Human Factors in Computing Systems (CHI), ACM Press, 2005, pp. 1403-1406.
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