Data security analysis using unsupervised learning and explanations

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3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaInnovations in Hybrid Intelligent Systems
EditoresEmilio Corchado, Juan Corchado, Ajith Abraham
Páginas112-119
Número de páginas8
DOI
EstadoPublicada - 2007

Serie de la publicación

NombreAdvances in Soft Computing
Volumen44
ISSN (versión impresa)1615-3871
ISSN (versión digital)1860-0794

Huella

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