Intelligent classification and visualization of network scans

C. Muelder, L. Chen, R. Thomason, Kwan-Liu Ma, T. Bartoletti

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)237-253
Number of pages17
JournalMathematics and Visualization
StatePublished - Jan 1 2008
Event4th International Workshop on Computer Security, VizSec 2007 - Sacramento, CA, United States
Duration: Oct 29 2007Oct 29 2007

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics


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