Transfer Learning for Phishing Detection: Screenshot-Based Website Classification

dc.authorscopusid 58705861300
dc.authorscopusid 57964038500
dc.authorscopusid 6507328166
dc.contributor.author Ecevit, Mert İlhan
dc.contributor.author Ecevit, M.I.
dc.contributor.author Daǧ, H.
dc.contributor.other Management Information Systems
dc.date.accessioned 2025-02-15T19:38:32Z
dc.date.available 2025-02-15T19:38:32Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp Çolhak F., CCIP, Center for Cyber Security and Critical Infrastructure Protection, Kadir Has University, Istanbul, Turkey; Ecevit M.I., CCIP, Center for Cyber Security and Critical Infrastructure Protection, Kadir Has University, Istanbul, Turkey; Daǧ H., CCIP, Center for Cyber Security and Critical Infrastructure Protection, Kadir Has University, Istanbul, Turkey en_US
dc.description.abstract Phishing remains a significant threat in the evolving cybersecurity landscape as phishing websites become increasingly similar to legitimate websites, complicating detection using traditional methods. This study explores AI-based solutions for screenshot-based phishing detection, utilizing the MTLP dataset and applying transfer learning with pretrained models (DenseNet, ResNet, EfficientNet, Inception, MobileNet, VGG) using the timm library. The study also discusses challenges related to phishing datasets and compares publicly available datasets, highlighting MTLP Dataset's strengths. DenseNetBlur121D was identified as the top-performing model, achieving an accuracy of 95.28%, a recall of 95.38%, a precision of 93.42%, and an F1 score of 94.39% when applied to the entire MTLP dataset. Both the model code and dataset are publicly available, providing a valuable resource for further research and development in this domain. © 2024 IEEE. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/UBMK63289.2024.10773490
dc.identifier.endpage 789 en_US
dc.identifier.isbn 9798350365887
dc.identifier.scopus 2-s2.0-85215517377
dc.identifier.scopusquality N/A
dc.identifier.startpage 784 en_US
dc.identifier.uri https://doi.org/10.1109/UBMK63289.2024.10773490
dc.identifier.uri https://hdl.handle.net/20.500.12469/7194
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering -- 9th International Conference on Computer Science and Engineering, UBMK 2024 -- 26 October 2024 through 28 October 2024 -- Antalya -- 204906 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Cybersecurity en_US
dc.subject Dataset Comparison en_US
dc.subject Open Dataset en_US
dc.subject Phishing Detection en_US
dc.subject Pretrained en_US
dc.subject Transfer Learning en_US
dc.title Transfer Learning for Phishing Detection: Screenshot-Based Website Classification en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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