Multiobjective evolutionary clustering approach to security vulnerability assesments

G. Corral*, A. Garcia-Piquer, A. Orriols-Puig, A. Fornells, E. Golobardes

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

Network vulnerability assessments collect large amounts of data to be further analyzed by security experts. Data mining and, particularly, unsupervised learning can help experts analyze these data and extract several conclusions. This paper presents a contribution to mine data in this security domain. We have implemented an evolutionary multiobjective approach to cluster data of security assessments. Clusters hold groups of tested devices with similar vulnerabilities to detect hidden patterns. Two different metrics have been selected as objectives to guide the discovery process. The results of this contribution are compared with other single-objective clustering approaches to confirm the value of the obtained clustering structures.

Idioma originalInglés
Título de la publicación alojadaHybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings
Páginas597-604
Número de páginas8
DOI
EstadoPublicada - 2009
Evento4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009 - Salamanca, Espana
Duración: 10 jun 200912 jun 2009

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5572 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009
País/TerritorioEspana
CiudadSalamanca
Período10/06/0912/06/09

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