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Título : Visualization and clustering for SNMP intrusion detection
Autor : Sánchez, Raúl
Herrero, Alvaro
Corchado, Emilio
Publicado en: Cybernetics and Systems: An International Journal. 2013, V. 44, n. 6-7, p. 505-532
Editorial : Taylor & Francis
Fecha de publicación : oct-2013
ISSN : 0196-9722
DOI: 10.1080/01969722.2013.803903
Resumen : Accurate intrusion detection is still an open challenge. The present work aims at being one step toward that purpose by studying the combination of clustering and visualization techniques. To do that, the mobile visualization connectionist agent-based intrusion detection system (MOVICAB-IDS), previously proposed as a hybrid intelligent IDS based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the simple network management network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in the present article. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection
Palabras clave: Automatic response
Clustering
Computational intelligence
Exploratory projection pursuit
k-means
Network intrusion detection
Materia: Informática
Computer science
URI : http://hdl.handle.net/10259/3859
Versión del editor: http://dx.doi.org/10.1080/01969722.2013.803903
Aparece en las colecciones: Artículos GICAP

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