{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:52:18Z","timestamp":1778169138891,"version":"3.51.4"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":285,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>The rapid digitization of financial service companies and organizations has heightened concerns over data security, particularly regarding unauthorized access to personal, financial, and password information. Among the various cyber threats, phishing attacks remain a pervasive and evolving challenge, exploiting deceptive emails, URLs, and SMS messages to illicitly harvest sensitive user data. In response, this research investigates a machine learning and deep learning\u2013based framework for comprehensive phishing detection across multiple phishing datasets. We integrated diverse datasets, including phishing emails from Kaggle, malicious SMS messages from Mendeley, and both phishing and legitimate URLs from Kaggle and OpenPhish, to train the proposed models. Leveraging this carefully preprocessed and heterogeneous dataset, we systematically evaluate and compare the performance of nine state\u2010of\u2010the\u2010art machine learning and deep learning models. Furthermore, we propose an ensemble approach that synergizes the strengths of these models to enhance detection accuracy. Our experimental results demonstrate exceptional performance, with support vector machine (SVM) and bidirectional gated recurrent unit (BiGRU) models achieving the highest detection accuracies of 99.91% and 99.75%, respectively. The findings indicate that deep learning architectures generally outperform traditional machine learning techniques in identifying phishing attempts. This work not only provides a robust, multimodal phishing detection solution but also offers valuable insights for strengthening cybersecurity defenses in an increasingly digital world.<\/jats:p>","DOI":"10.1155\/acis\/6633979","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T07:38:03Z","timestamp":1760341083000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Phishing Detection: A Machine Learning Approach to Predicting Malicious Emails, URLs, and SMS Messages"],"prefix":"10.1155","volume":"2025","author":[{"given":"Mehraj Ibne","family":"Halim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Zibran","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4690-158X","authenticated-orcid":false,"given":"Md. 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AlsarhanA. Al-FraihatD.et al. Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2023 2nd International Engineering Conference on Electrical Energy and Artificial Intelligence (EICEEAI) 2023 Zarqa Jordan 1\u20136 https:\/\/doi.org\/10.1109\/eiceeai60672.2023.10590011.","DOI":"10.1109\/EICEEAI60672.2023.10590011"},{"key":"e_1_2_14_34_2","doi-asserted-by":"crossref","unstructured":"AlsarhanA. IgriedB. SaleemR. M. B. AlauthmanM. andAljaidiM. 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