Publication: Evolving Systems for Computer User Behavior Classification
dc.affiliation.dpto | UC3M. Departamento de Informática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS) | es |
dc.contributor.author | Iglesias Martínez, José Antonio | |
dc.contributor.author | Ledezma Espino, Agapito Ismael | |
dc.contributor.author | Sanchis de Miguel, María Araceli | |
dc.date.accessioned | 2016-09-22T09:57:16Z | |
dc.date.available | 2016-09-22T09:57:16Z | |
dc.date.issued | 2013 | |
dc.description.abstract | A computer can keep track of computer users to improve the security in the system. However, this does not prevent a user from impersonating another user. Only the user behavior recognition can help to detect masqueraders. Under the UNIX operating system, users type several commands which can be analyzed in order to create user profiles. These profiles identify a specific user or a specific computer user behavior. In addition, a computer user behavior changes over time. If the behavior recognition is done automatically, these changes need to be taken into account. For this reason, we propose in this paper a simple evolving method that is able to keep up to date the computer user behavior profiles. This method is based on Evolving Fuzzy Systems. The approach is evaluated using real data streams. | en |
dc.description.sponsorship | This work has been supported by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03). | en |
dc.format.extent | 7 | |
dc.format.mimetype | application/pdf | |
dc.identifier.bibliographicCitation | Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on. : IEEE Computer Society. Pp. 78-83 | |
dc.identifier.isbn | 978-1-4673-5855-2 | |
dc.identifier.publicationfirstpage | 78 | |
dc.identifier.publicationlastpage | 83 | |
dc.identifier.publicationtitle | Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on | |
dc.identifier.uri | https://hdl.handle.net/10016/23611 | |
dc.identifier.uxxi | CC/0000021693 | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.eventdate | 16-19 April 2013 | en |
dc.relation.eventplace | Singapore | en |
dc.relation.eventtitle | 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS | |
dc.relation.projectID | Gobierno de España. TRA2011-29454-C03-03 | es |
dc.relation.projectID | Gobierno de España. TRA2013-48314-C3-1-R | es |
dc.relation.publisherversion | http://dx.doi.org/10.1109/EAIS.2013.6604108 | |
dc.rights | © 2013 IEEE | |
dc.rights.accessRights | open access | |
dc.subject.eciencia | Informática | es |
dc.subject.other | Prototypes | en |
dc.subject.other | Computers | en |
dc.subject.other | Computational modeling | en |
dc.subject.other | Hidden Markov models | en |
dc.subject.other | Training | en |
dc.subject.other | Data models | en |
dc.subject.other | Mathematical model | en |
dc.title | Evolving Systems for Computer User Behavior Classification | en |
dc.type | conference paper | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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