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PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach

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Published in:Cybernetics and Information Technologies
Format: Online Article RSS Article
Published: 2026
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container_title Cybernetics and Information Technologies
description
discipline_display Technology & Engineering
discipline_facet Technology & Engineering
format Online Article
RSS Article
genre Journal Article
id rss_article:25789
institution FRELIP
journal_source_facet Cybernetics and Information Technologies
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
Internet
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Internet
Technology & Engineering — Computing
Technology & Engineering
Internet
Technology & Engineering — Computing
Technology & Engineering
subject_facet Internet
Technology & Engineering — Computing
Technology & Engineering
title PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_auth PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_full PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_fullStr PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_full_unstemmed PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_short PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach
title_sort phishfusionnet: a wide and deep phishing detection with a hybrid learning approach
topic Internet
Technology & Engineering — Computing
Technology & Engineering
url https://sciendo.com/article/10.2478/cait-2026-0008