TitleExplanations of unsupervised learning clustering applied to data security analysis
Publication TypeJournal Article
Year of Publication2009
AuthorsCorral G, Armengol E, Fornells A, Golobardes E
NumberIn press
Date Published2762

Network security tests should be periodically conducted to detect vulnerabilities before they are exploited. However, analysis of testing results is resource intensive with many data and requires expertise because it is an unsupervised domain. This paper presents how to automate and improve this analysis through the identification and explanation of device groups with similar vulnerabilities. Clustering is used for discovering hidden patterns and abnormal behaviours. Self-Organizing Maps are preferred due to their soft computing capabilities. Explanations based on Anti-unification give comprehensive descriptions of clustering results to analysts. This approach is integrated in Analia, a computer-aided system to detect network vulnerabilities.