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dc.rights.licenseAttribution 4.0 International
dc.contributor.authorVelez de Mendizabal, Iñaki
dc.contributor.authorVidriales Mazorriaga, Xabier
dc.contributor.authorEzpeleta, Iñigo
dc.contributor.authorZurutuza, Urko
dc.contributor.otherBasto-Fernandes, Vitor
dc.contributor.otherR. Méndez, José
dc.date.accessioned2024-02-02T08:53:20Z
dc.date.available2024-02-02T08:53:20Z
dc.date.issued2023
dc.identifier.issn1989-1660
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173976
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6248
dc.description.abstractThe evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use of Leetspeak are concrete and clear examples of techniques currently used to bypass filters. Despite the importance of dealing with these problems, the number of studies to solve them is quite small, and the reported performance is very limited. This study reviews the work done so far (very rudimentary) for Leetspeak deobfuscation and proposes a new technique based on using neural networks for decoding purposes. In addition, we distribute an image database specifically created for training Leetspeak decoding models. We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes. Using these corpora, we have experimentally evaluated our neural network approach for decoding Leetspeak. The results obtained have shown the usefulness of the proposed model for addressing the deobfuscation of Leetspeak character sequences.en
dc.language.isoeng
dc.publisherUNIR - Universidad Internacional de La Rioja
dc.rights© 2023 UNIR
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceInternational Journal of Interactive Multimedia and Artificial Intelligence
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectLeetspeak
dc.subjectODS 9 Industria, innovación e infraestructura
dc.subjectSpam Filtering
dc.subjectText Deobfuscation
dc.titleDeobfuscating leetspeak with deep learning to improve spam filtering
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
local.contributor.groupAnálisis de datos y ciberseguridad
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.9781/ijimai.2023.07.003
local.rights.publicationfeeAPC
local.rights.publicationfeeamount1300 EUR
local.contributor.otherinstitutionhttps://ror.org/014837179
local.contributor.otherinstitutionhttps://ror.org/05rdf8595
local.contributor.otherinstitutionInstituto de Investigación Sanitaria Galicia Sur (IISGS)
local.source.detailsVol. 8. N. 4. Pp. 46-55
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderNameGobierno de España
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/038jjxj40 http://data.crossref.org/fundingdata/funder/10.13039/501100010198
oaire.fundingStreamIkertalde Convocatoria 2022-2025
oaire.fundingStreamPrograma Estatal de Investigación, Desarrollo e Innovación orientada a los retos de la sociedad en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016, convocatoria del 2017
oaire.awardNumberIT1676-22
oaire.awardNumberTIN2017-84658-C2-2-R
oaire.awardTitleGrupo de sistemas inteligentes para sistemas industriales
oaire.awardTitleIntegración de Conocimiento Semántico para el Filtrado de Spam basado en Contenido (SKI4SPAM)
oaire.awardURISin información
oaire.awardURISin información


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