Phishing Detection Using Deep Learning Attention Techniques

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but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorSafonov, Yehor
dc.date.accessioned2023-01-06T10:05:43Z
dc.date.available2023-01-06T10:05:43Z
dc.date.issued2021cs
dc.description.abstractIn the modern world, electronic communication is defined as the most used technologyfor exchanging messages between users. The growing popularity of emails brings about considerablesecurity risks and transforms them into an universal tool for spreading phishing content. Even thoughtraditional techniques achieve high accuracy during spam filtering, they do not often catch up to therapid growth and evolution of spam techniques. These approaches are affected by overfitting issues,may converge into a poor local minimum, are inefficient in high-dimensional data processing andhave long-term maintainability problems. The main contribution of this paper is to develop and trainadvanced deep networks which use attention mechanisms for efficient phishing filtering and text understanding.Key aspects of the study lie in a detailed comparison of attention based machine learningmethods, their specifics and accuracy during the application to the phishing problem. From a practicalpoint of view, the paper is focused on email data corpus preprocessing. Deep learning attention basedmodels, for instance the BERT and the XLNet, have been successfully implemented and comparedusing statistical metrics. Obtained results show indisputable advantages of deep attention techniquescompared to the common approaches.en
dc.formattextcs
dc.format.extent131-135cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 131-135. ISBN 978-80-214-5943-4cs
dc.identifier.doi10.13164/eeict.2021.131
dc.identifier.isbn978-80-214-5943-4
dc.identifier.urihttp://hdl.handle.net/11012/200827
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 27st Conference STUDENT EEICT 2021: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectartificial intelligenceen
dc.subjectattention mechanismen
dc.subjectdeep learningen
dc.subjectNLPen
dc.subjectphishing filteringen
dc.subjecttextclassificationen
dc.subjecttransformersen
dc.titlePhishing Detection Using Deep Learning Attention Techniquesen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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