Show simple item record

dc.contributor.authorSánchez, Raúl
dc.contributor.authorHerrero Cosío, Álvaro
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.date.accessioned2017-09-05T11:01:22Z
dc.date.available2017-09-05T11:01:22Z
dc.date.issued2013/10
dc.identifier.citationCybernetics and Systems. Volumen 44 (6-7), pp. 505-532. Informa UK Limited.
dc.identifier.issn0196-9722 (Print) / 1087-6553 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/134351
dc.description.abstractAccurate 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInforma UK Limited
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleVisualizationi and clustering for SNMP intrusion detection
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported