Antiphishing model based on similarity index and neural networks

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dc.contributor.authorSharma, Bhawna
dc.contributor.authorSingh, Parvinder
dc.contributor.authorKaur, Jasvinder
dc.contributor.authorBringas García, Pablo
dc.date.accessioned2025-11-05T11:37:24Z
dc.date.available2025-11-05T11:37:24Z
dc.date.issued2019-10-30
dc.date.updated2025-11-05T11:37:24Z
dc.description.abstractPhishing is a negative technique that is used to steel private and confidential information over the web. In the present work author proposed a hybrid similarity of Cosine and Soft Cosine to calculate the similarity between the user query and repository as an anti-phishing approach. The proposed work model uses a multiclass learning method called Feed Forward Back Propagation Neural Network. The model evaluation results with 100 to 3000 test files shows that the hybrid model is able to detect the phishing attack with an average precision of 71% and is highly effective.en
dc.identifier.citationSharma, B., Singh, P., Kaur, J., & Bringas, P. G. (2019). Antiphishing model based on similarity index and neural networks. International Journal of Engineering and Advanced Technology, 9(1), 4114-4119. https://doi.org/10.35940/IJEAT.A1350.109119
dc.identifier.doi10.35940/IJEAT.A1350.109119
dc.identifier.eissn2249-8958
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4282
dc.language.isoeng
dc.publisherBlue Eyes Intelligence Engineering and Sciences Publication
dc.rights© The Authors
dc.subject.otherNeural Network
dc.subject.otherPhishing
dc.subject.otherSimilarity
dc.titleAntiphishing model based on similarity index and neural networksen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage4119
oaire.citation.issue1
oaire.citation.startPage4114
oaire.citation.titleInternational Journal of Engineering and Advanced Technology
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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