{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:27:26Z","timestamp":1776680846566,"version":"3.51.2"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T00:00:00Z","timestamp":1744502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The escalating prevalence of phishing attacks in recent years has underscored the imperative need for a comprehensive and sophisticated response to mitigate this pervasive cyber threat. Characterized by deceptive tactics to obtain sensitive information, phishing has evolved in sophistication, resulting in severe consequences such as financial loss, identity theft, and compromise of personal data. This research addresses the inherent challenges faced by existing anti-phishing solutions, encompassing limitations in feature extraction methodologies, suboptimal feature selection, and issues related to dataset imbalance. In response to these challenges, we propose \u201cPHISH_ATTENTION,\u201d an advanced anti-phishing framework that integrates Variational Autoencoders and a Multi-Head Self-Attention Mechanism. The framework is further enhanced by the incorporation of a Deep Convolutional Generative Adversarial Network to effectively address dataset imbalances. Rigorous testing on benchmark datasets reveals that PHISH_ATTENTION achieves a peak detection accuracy of 98.57% with a minimal false alarm rate of 1.09%, surpassing prevailing anti-phishing models. Distinguished by its proficiency in real-time phishing website detection, the framework\u2019s autonomous acquisition of significant URL features establishes it as a resilient and pioneering contribution to the cybersecurity domain.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf036","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:27:29Z","timestamp":1742430449000},"page":"1263-1284","source":"Crossref","is-referenced-by-count":2,"title":["PHISH_ATTENTION: achieving robust phishing website detection with balanced datasets and advanced URL features"],"prefix":"10.1093","volume":"68","author":[{"given":"Manoj Kumar","family":"Prabhakaran","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Mepco Schlenk Engineering College , Sivakasi, Virudhunagar-626005, Tamilnadu ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abinaya Devi","family":"Chandrasekar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mepco Schlenk Engineering College , Sivakasi, Virudhunagar-626005, Tamilnadu ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parvathy","family":"Meenakshi Sundaram","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sethu Institute of Technology, Kariapatti , Virudhunagar-626115, Tamilnadu ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"key":"2025092201571241700_ref1","doi-asserted-by":"publisher","first-page":"15196","DOI":"10.1109\/ACCESS.2019.2892066","article-title":"Phishing website detection based on multidimensional features driven by deep learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"2025092201571241700_ref2"},{"key":"2025092201571241700_ref3","volume-title":"Google Safe Browsing APIs","year":"2018"},{"key":"2025092201571241700_ref4","volume-title":"Proceedings of the 6th Conference on Email and Anti-Spam (CEAS)","author":"Sheng"},{"key":"2025092201571241700_ref5","first-page":"1","volume-title":"2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS)","author":"Aravindhan","year":"2016"},{"key":"2025092201571241700_ref6","first-page":"492","volume-title":"2012 International Conference for Internet Technology and Secured Transactions","author":"Mohammad","year":"2012"},{"key":"2025092201571241700_ref7","doi-asserted-by":"crossref","first-page":"103288","DOI":"10.1016\/j.advengsoft.2022.103288","article-title":"Phishing URL detection using machine learning methods","volume":"173","author":"Ahammad","year":"2022","journal-title":"Advances in Engineering Software"},{"key":"2025092201571241700_ref8","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.procs.2018.03.053","article-title":"Detecting phishing websites via aggregation analysis of page layouts","volume":"129","author":"Mao","year":"2018","journal-title":"Procedia Computer Science"},{"key":"2025092201571241700_ref9","doi-asserted-by":"publisher","first-page":"17020","DOI":"10.1109\/ACCESS.2017.2743528","article-title":"Phishing-alarm: robust and efficient phishing detection via page component similarity","volume":"5","author":"Mao","year":"2017","journal-title":"IEEE Access"},{"key":"2025092201571241700_ref10","first-page":"684","volume-title":"International Conference on Human-Computer Interaction June 26","author":"Sharma","year":"2022"},{"key":"2025092201571241700_ref11","first-page":"55","volume-title":"Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics Association for Computing Machinery (ACM)","author":"Verma","year":"2017"},{"key":"2025092201571241700_ref12","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders. 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