Data security analysis using unsupervised learning and explanations

Research output: Book chapterConference contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationInnovations in Hybrid Intelligent Systems
EditorsEmilio Corchado, Juan Corchado, Ajith Abraham
Number of pages8
Publication statusPublished - 2007

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794


  • Clustering
  • Explanations
  • Network security
  • Unsupervised learning


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