{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T03:50:03Z","timestamp":1770954603729,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Korea government","award":["RS-2022-II220113"],"award-info":[{"award-number":["RS-2022-II220113"]}]},{"name":"Yonsei Fellow Program funded by Lee Youn Jae"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Phishing attacks are a significant issue in cybersecurity, and extensive efforts with machine learning methods have been made to detect them. The high similarity between benign and phishing URLs requires a substantial amount of data for effective training, but ever-changing attacks hinder us from preparing for sufficient data. To cope with the issue of limited data, this paper proposes a triplet-sampled prototypical network, which learns the characteristics of URL samples for various phishing classes with appropriate prototypes trained with contrastive learning. For a new input URL, the learned model finds the most similar prototypes contrasted by themselves and calculates the average distance to judge whether it is phishing or not. Experiments with the three benchmark datasets of ISCX-URL-2016, PhishStorm, and PhishTank show an impressive performance of 99.83%, 98.61%, and 97.79%, respectively. Additional experiment with 100-, 10-, 5-, and 1-shot scenarios demonstrates that the proposed method allows for efficient and effective detection of phishing URLs even with a limited number of training data.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaf023","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T07:59:36Z","timestamp":1745567976000},"source":"Crossref","is-referenced-by-count":0,"title":["Contrastive learning of prototypical network for detecting phishing URL"],"prefix":"10.1093","volume":"34","author":[{"given":"Jaeil","family":"Park","sequence":"first","affiliation":[{"name":"Department of Computer Science, Yonsei University , Seoul 03722 ,","place":["South Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University , Seoul 03722 ,","place":["South Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"2026021221581666000_ref1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.47893\/IJSSAN.2022.1221","article-title":"Prevention of phishing attacks using AI-based cybersecurity awareness training","volume":"3","author":"Sharma","year":"2022","journal-title":"IJSSAN"},{"key":"2026021221581666000_ref2","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1109\/TIFS.2022.3164212","article-title":"Phishsim: aiding phishing website detection with a feature-free tool","volume":"17","author":"Purwanto","year":"2022","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2026021221581666000_ref3","doi-asserted-by":"publisher","first-page":"36429","DOI":"10.1109\/ACCESS.2022.3151903","article-title":"Deep learning for phishing detection: taxonomy, current challenges and future directions","volume":"10","author":"Do","year":"2022","journal-title":"IEEE Access"},{"key":"2026021221581666000_ref4","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1177\/10439862211001628","article-title":"Cyber place management and crime prevention: the effectiveness of cybersecurity awareness training against phishing attacks","volume":"37","author":"Back","year":"2021","journal-title":"J Contemp Crim Justice"},{"key":"2026021221581666000_ref5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.comcom.2021.04.023","article-title":"A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment","volume":"175","author":"Gupta","year":"2021","journal-title":"Comput Commun"},{"key":"2026021221581666000_ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116239","article-title":"Piracema. 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