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Proceedings Paper

Entropy-based heavy tailed distribution transformation and visual analytics for monitoring massive network traffic
Author(s): Keesook J. Han; Matthew Hodge; Virginia W. Ross
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Paper Abstract

For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

Paper Details

Date Published: 2 June 2011
PDF: 10 pages
Proc. SPIE 8019, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X, 80190B (2 June 2011); doi: 10.1117/12.884388
Show Author Affiliations
Keesook J. Han, U.S. Air Force Research Lab. (United States)
Matthew Hodge, Rochester Institute of Technology (United States)
Virginia W. Ross, U.S. Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8019:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X
Edward M. Carapezza, Editor(s)

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