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Proceedings Paper

Configurable IP-space maps for large-scale, multi-source network data visual analysis and correlation
Author(s): Scott Miserendino; Corey Maynard; William Freeman
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Paper Abstract

The need to scale visualization of cyber (IP-space) data sets and analytic results as well as to support a variety of data sources and missions have proved challenging requirements for the development of a cyber common operating picture. Typical methods of visualizing IP-space data require unreliable domain conversions such as IP geolocation, network topology that is difficult to discover, or data sets that can only display one at a time. In this work, we introduce a generalized version of hierarchical network maps called configurable IP-space maps that can simultaneously visualize multiple layers of IP-based data at global scale. IP-space maps allow users to interactively explore the cyber domain from multiple perspectives. A web-based implementation of the concept is described, highlighting a novel repurposing of existing geospatial mapping tools for the cyber domain. Benefits of the configurable IP-space map concept to cyber data set analysis using spatial statistics are discussed. IP-space map structure is found to have a strong effect on data clustering behavior, hinting at the ability to automatically determine concentrations of network events within an organizational hierarchy.

Paper Details

Date Published: 3 February 2014
PDF: 14 pages
Proc. SPIE 9017, Visualization and Data Analysis 2014, 901705 (3 February 2014); doi: 10.1117/12.2037862
Show Author Affiliations
Scott Miserendino, Northrop Grumman Corp. (United States)
Corey Maynard, Northrop Grumman Corp. (United States)
William Freeman, Northrop Grumman Corp. (United States)


Published in SPIE Proceedings Vol. 9017:
Visualization and Data Analysis 2014
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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