Data Security Analysis Using Unsupervised Learning and Explanations

  • G. Corral
  • E. Armengol
  • A. Fornells
  • E. Golobardes
Part of the Advances in Soft Computing book series (AINSC, volume 44)

Abstract

Vulnerability assessment is an effective security mechanism to identify vulnerabilities in systems or networks before they are exploited. However manual analysis of network test and vulnerability assessment results is time consuming and demands expertise. This paper presents an improvement of Analia, which is a security system to process results obtained after a vulnerability assessment using artificial intelligence techniques. The system applies unsupervised clustering techniques to discover hidden patterns and extract abnormal device behaviour by clustering devices in groups that share similar vulnerabilities. The proposed improvement consists in extracting a symbolic explanation for each cluster in order to help security analysts to understand the clustering solution using network security lexicon.

Keywords

Network Security Unsupervised Learning Clustering Explanations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • G. Corral
    • 1
  • E. Armengol
    • 2
  • A. Fornells
    • 1
  • E. Golobardes
    • 1
  1. 1.Grup de Recerca en Sistemes Intelligents Enginyeria i Arquitectura La SalleUniversitat Ramon LlullBarcelonaSpain
  2. 2.IIIA, Artificial Intelligence Research InstituteCSIC, Spanish Council for Scientific ResearchBellaterra, BarcelonaSpain

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