Cohesion factors: Improving the clustering capabilities of consensus

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

5 Cites (Scopus)

Resum

Security has become a main concern in corporate networks. Security tests are essential to identify vulnerabilities, but experts must analyze very large data and complex information. Unsupervised learning can help by clustering groups of devices with similar vulnerabilities. However an index to evaluate every solution should be calculated to demonstrate results validity. Also the value of the number of clusters should be tuned for every data set in order to find the best solution. This paper introduces SOM as a clustering method to evaluate complex and uncertain knowledge in Consensus, a distributed security system for vulnerability testing; it proposes new metrics to evaluate the cohesion of every cluster, and also the cohesion between clusters; it applies unsupervised algorithms and validity metrics to a security data set; and it presents a method to obtain the best number of clusters regarding these new cohesion metrics: Intracohesion and Intercohesion factors.

Idioma originalAnglès
Títol de la publicacióIntelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
EditorSpringer Verlag
Pàgines488-495
Nombre de pàgines8
ISBN (imprès)3540454853, 9783540454854
DOIs
Estat de la publicacióPublicada - 2006
Esdeveniment7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, Spain
Durada: 20 de set. 200623 de set. 2006

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum4224 LNCS
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

Conferència

Conferència7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
País/TerritoriSpain
CiutatBurgos
Període20/09/0623/09/06

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