VizSEC 2007 pp 237-253

Part of the Mathematics and Visualization book series (MATHVISUAL) | Cite as

Intelligent Classification and Visualization of Network Scans

  • C. Muelder
  • L. Chen
  • R. Thomason
  • K. -L. Ma
  • T. Bartoletti

Abstract

Network scans are a common first step in a network intrusion attempt. In order to gain information about a potential network intrusion, it is beneficial to analyze these network scans. Statistical methods such as wavelet scalogram analysis have been used along with visualization techniques in previous methods. However, applying these statistical methods causes a substantial amount of data loss. This paper presents a study of using associative memory learning techniques to directly compare network scans in order to create a classification which can be used by itself or in conjunction with existing visualization techniques to better characterize the sources of these scans. This produces an integrated system of visual and intelligent analysis which is applicable to real world data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • C. Muelder
    • 1
  • L. Chen
    • 1
  • R. Thomason
    • 1
  • K. -L. Ma
    • 1
  • T. Bartoletti
    • 2
  1. 1.University of CaliforniaDavis
  2. 2.Lawrence Livermore National LaboratoryUSA

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