{ "ok": true, "doi": "10.46243/jst.2023.v8.i06.pp158-162", "doi_display": "10.46243/jst.2023.v8.i06.pp158-162", "doi_url": "https://doi.org/10.46243/jst.2023.v8.i06.pp158-162", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/757", "title": "Effective And Efficient Detection Of Phishing Emails Using Machine Learning", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:28", "updated_at": "2026-09-29 23:59:47", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i06.pp158-162", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Effective And Efficient Detection Of Phishing Emails Using Machine Learning", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i06.pp158-162" } ], "agents": [ { "role": "author", "name": { "given": "Moola.Akshitha", "family": "Moola.Akshitha" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-08-07", "date_type": "PublicationDate", "online": "2023-08-07" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "7", "pages": { "first": "158", "last": "162" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/757", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/757/682", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/757/1769", "purpose": "text-mining", "return_type": "application/xml" } ], "abstract": { "value": "Emails are widely used for personal and professional communication,often involving the transmission of sensitive information like banking details,credit reports,and login data.Consequently,these emails become valuable targets for cyber criminals who seek to exploit such knowledge for their own malicious purposes.Phishing, a deceptive technique employed by these individuals,involves impersonating well-known sources to deceive and extract sensitive information from unsuspecting individuals.The sender of a phishing email uses false pretenses to persuade recipients into disclosed personal information.In this work,the detection of phishing emails is learning methods to categorize emails as either genuine or phishing attempts.LMT classifiers have proven highly effective in accurately classifying emails,achieving optimal accuracy in email classification tasks.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2023-08-07", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1016/j.cose.2017.04.006", "unstructured": "Aleroud,A.,&zhou,L.(2017).Phishing e n v i r o n m e n t s, t e c h n i q u e s, a n d countermeasures:A survey.Computers &security,68,160-196" }, { "key": "ref2", "doi": "10.1016/s1361-3723(18)30007-1", "unstructured": "Vayansky,I.,&Kumar,S.(2018).Phishingchallenges and solutions.Computer Fraud & Security,2018,15-20" }, { "key": "ref3", "doi": "10.1016/j.ijhcs.2018.06.004", "unstructured": "E. J. Williams and colleagues, “Exploring susceptibility to phishing in the workplace,” International Journal of Human-Computer Studies, vol. 120, no. 1, 2018, pp. 1–13" }, { "key": "ref4", "doi": "10.1109/ccwc51732.2021.9375997", "unstructured": "A. Odeh et al., “Machine Learning Techniques for Detection of Website Phishing: A Review for Promises and Challenges,” IEEE, 2021, 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0813- 0818" }, { "key": "ref5", "unstructured": "Effective Detection of Phishing Websites Using Multilayer Perceptron, A. Odeh et al., 2020" }, { "key": "ref6", "doi": "10.5455/jjcit.71-1600061738", "unstructured": "“PHIBOOST-a novel phishing detection model using Adaptive boosting approach,” Jordanian Journal of Computers and Information Technology (JJCIT), vol. 7, 2021" }, { "key": "ref7", "doi": "10.1016/j.eswa.2018.03.050", "unstructured": "K. L.Chiew et al. (2018) “A survey of phishing attacks: Their types, vectors, and technical approaches,” Expert Systems with Applications, vol. 106, pp. 1–20" }, { "key": "ref8", "doi": "10.34028/iajit/17/4/7", "unstructured": "M. Al-Fayoumi et al., “Intelligent association classification technique for phishing website detection,” International Arab Journal of Information Technology, vol. 17, 2020, pp. 488–496" }, { "key": "ref9", "doi": "10.1016/j.tele.2020.101343", "unstructured": "Why do consumers not report spear phishing emails? [9] Y. Kwak et al., Telematics and Informatics, vol. 48, p. 101343, 2020" }, { "key": "ref10", "unstructured": "“PHISHING WEBSITE DETECTION USING MULTILAYER PERCEPTRON,” A. 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