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dc.contributor.authorHerrero Cosío, Álvaro
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorGastaldo, Paolo
dc.contributor.authorPicasso, Francesco
dc.contributor.authorZunino, Rodolfo
dc.date.accessioned2017-09-06T09:15:50Z
dc.date.available2017-09-06T09:15:50Z
dc.date.issued2007/06
dc.identifier.citationIndustrial Electronics, 2007. ISIE 2007. IEEE International Symposium on. pp. 1905 - 1910.
dc.identifier.isbn978-1-4244-0754-5 (Print) / 978-1-4244-0755-2 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135024
dc.description.abstractIntrusion detection systems (IDS's) ensure the security of computer networks by monitoring traffic and generating alerts, or taking actions, when suspicious activities are detected. This paper proposes a network-based IDS supporting an intuitive visualization of the time evolution of network traffic. The system is designed to assist the network manager in detecting anomalies, and exploits auto-associative back-propagation (AABP) neural networks to turn raw data extracted from traffic sources into an intuitive 2D representation. The neural component operates as a sort of smart compression operator and supports a compact representation of multi-dimensional data. The empirical verification of the mapping method involved the detection of anomalies in traffic ascribed to the simple network management protocol (SNMP), and confirmed the validity of the proposed approach.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleAuto-Associative Neural Techniques for Intrusion Detection Systems
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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