{ "ok": true, "doi": "10.46243/jst.2020.v5.i6.pp11-18", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:23", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": ": In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc." }, { "path": "agents.0.name.org", "from": null, "to": "Longman Publishers" }, { "path": "agents.0.role", "from": null, "to": "publisher" }, { "path": "characters.0", "from": null, "to": "Language" }, { "path": "container.identifiers.0.medium", "from": null, "to": "electronic" }, { "path": "container.identifiers.0.type", "from": null, "to": "ISSN" }, { "path": "container.identifiers.0.value", "from": null, "to": "2456-5660" }, { "path": "container.issue", "from": null, "to": "Volume 5" }, { "path": "container.pages.first", "from": null, "to": "11" }, { "path": "container.pages.last", "from": null, "to": "18" }, { "path": "container.titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "container.titles.0.value", "from": null, "to": "Journal of Science & Technology" }, { "path": "container.type", "from": null, "to": "Journal" }, { "path": "dates.date_type", "from": null, "to": "PublicationDate" }, { "path": "dates.online", "from": null, "to": "2020-09-22" }, { "path": "dates.published", "from": null, "to": "2020-09-22" }, { "path": "doi", "from": null, "to": "10.46243/jst.2020.v5.i6.pp11-18" }, { "path": "format", "from": null, "to": "smartscholars-doi-metadata/1.0" }, { "path": "identifiers.0.type", "from": null, "to": "DOI" }, { "path": "identifiers.0.value", "from": null, "to": "10.46243/jst.2020.v5.i6.pp11-18" }, { "path": "language", "from": null, "to": "en" }, { "path": "license.applies_to", "from": null, "to": "vor" }, { "path": "license.start", "from": null, "to": "2020-09-22" }, { "path": "license.url", "from": null, "to": "https://creativecommons.org/licenses/by/4.0" }, { "path": "links.0.primary", "from": null, "to": true }, { "path": "links.0.return_type", "from": null, "to": "text/html" }, { "path": "links.0.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/view/512" }, { "path": "links.1.purpose", "from": null, "to": "text-mining" }, { "path": "links.1.return_type", "from": null, "to": "application/pdf" }, { "path": "links.1.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/512/456" }, { "path": "links.2.purpose", "from": null, "to": "text-mining" }, { "path": "links.2.return_type", "from": null, "to": "application/xml" }, { "path": "links.2.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/512/1526" }, { "path": "modes.0", "from": null, "to": "Visual" }, { "path": "record.issue_number", "from": null, "to": 1 }, { "path": "record.registered", "from": null, "to": "2020-10-29" }, { "path": "record.registrant", "from": null, "to": "Longman Publishers" }, { "path": "record.source", "from": null, "to": "crossref-api" }, { "path": "record.source_agency", "from": null, "to": "Crossref (member 25296)" }, { "path": "record.updated", "from": null, "to": "2026-09-17" }, { "path": "references.0.doi", "from": null, "to": "10.1007/978-3-642-33167-1_47" }, { "path": "references.0.key", "from": null, "to": "ref1" }, { "path": "references.0.unstructured", "from": null, "to": "R. 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Sahoo, “Data mining a way to solve phishing attacks,” in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1–5, IEEE, 2018" }, { "path": "references.9.doi", "from": null, "to": "10.1109/compsac.2005.126" }, { "path": "references.9.key", "from": null, "to": "ref10" }, { "path": "references.9.unstructured", "from": null, "to": "E. Kirda and C. Kruegel, “Protecting users against phishing attacks with antiphish,” in 29th Annual International Computer Software and Applications Conference (COMPSAC’05), vol. 1, pp. 517–524, IEEE, 2005" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "A Survey on Phishing Detection and The Importance of Feature\nSelection In Data Mining Classification Algorithms" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2020.v5.i6.pp11-18", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A Survey on Phishing Detection and The Importance of Feature\nSelection In Data Mining Classification Algorithms", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2020.v5.i6.pp11-18" } ], "agents": [ { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2020-09-22", "date_type": "PublicationDate", "online": "2020-09-22" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "issue": "Volume 5", "pages": { "first": "11", "last": "18" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/512", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/512/456", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/512/1526", "purpose": "text-mining", "return_type": "application/xml" } ], "abstract": { "value": ": In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2020-09-22", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1007/978-3-642-33167-1_47", "unstructured": "R. Verma, N. Shashidhar, and N. Hossain, “Detecting phishing emails the natural language way,” in European Symposium on Research in Computer Security, pp. 824–841, Springer, 2012" }, { "key": "ref2", "unstructured": "Apwg, apwg report 2019. https://docs.apwg. org/reports/apwg_trends_report_q1_2019. pdf/" }, { "key": "ref3", "doi": "10.1109/icecta.2017.8252051", "unstructured": "A. Subasi, E. Molah, F. Almkallawi, and T. J. Chaudhery, “Intelligent phishing website detection using random forest classifier,” in 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1–5, IEEE, 2017" }, { "key": "ref4", "doi": "10.1109/isdfs.2018.8355357", "unstructured": "M. Karabatak and T. Mustafa, “Performance comparison of classifiers on reduced phishing website dataset,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–5, IEEE, 2018" }, { "key": "ref5", "unstructured": "M. Lichman et al., “Uci machine learning repository,” 2013" }, { "key": "ref6", "doi": "10.1109/iceca.2018.8474820", "unstructured": "M. Thaker, M. Parikh, P. Shetty, V. Neogi, and S. Jaswal, “Detecting phishing websites using data mining,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1876–1879, IEEE, 2018" }, { "key": "ref7", "doi": "10.1145/1062745.1062868", "unstructured": "L. Wenyin, G. Huang, L. Xiaoyue, Z. Min, and X. Deng, “Detection of phishing webpages based on visual similarity,” in Special interest tracks and posters of the 14th international conference on World Wide Web, pp. 1060–1061, ACM, 2005" }, { "key": "ref8", "doi": "10.1016/j.jksuci.2017.07.005", "unstructured": "G. Sonowal and K. Kuppusamy, “Phidma–a phishing detection model with multi-filter approach,” Journal of King Saud UniversityComputer and Information Sciences, 2017" }, { "key": "ref9", "doi": "10.1109/icctct.2018.8550910", "unstructured": "P. K. Sahoo, “Data mining a way to solve phishing attacks,” in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1–5, IEEE, 2018" }, { "key": "ref10", "doi": "10.1109/compsac.2005.126", "unstructured": "E. Kirda and C. Kruegel, “Protecting users against phishing attacks with antiphish,” in 29th Annual International Computer Software and Applications Conference (COMPSAC’05), vol. 1, pp. 517–524, IEEE, 2005" }, { "key": "ref11", "doi": "10.1145/1143120.1143133", "unstructured": "M. Wu, R. C. Miller, and G. Little, “Web wallet: preventing phishing attacks by revealing user intentions,” in Proceedings of the second symposium on Usable privacy and security, pp. 102–113, ACM, 2006" }, { "key": "ref12", "doi": "10.1016/j.ijleo.2013.04.078", "unstructured": "Y. Li, R. Xiao, J. Feng, and L. Zhao, “A semi-supervised learning approach for detection of phishing webpages,” Optik, vol. 124, no. 23, pp. 6027–6033, 2013" }, { "key": "ref13", "doi": "10.1145/1143120.1143126", "unstructured": "K.-P. Yee and K. Sitaker, “Passpet: convenient password management and phishing protection,” in Proceedings of the second symposium on Usable privacy and security, pp. 32–43, ACM, 2006" }, { "key": "ref14", "doi": "10.1109/rteict.2017.8256835", "unstructured": "H. Sharma, E. Meenakshi, and S. K. Bhatia, “A comparative analysis and awareness survey of phishing detection tools,” in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1437–1442, IEEE, 2017" }, { "key": "ref15", "unstructured": "Y. Zhang, S. Egelman, L. Cranor, and J. Hong, “Phinding phish: Evaluating anti-phishing tools,” 2006" }, { "key": "ref16", "doi": "10.1109/icdma.2013.145", "unstructured": "T. Yue, J. Sun, and H. Chen, “Fine-grained mining and classification of malicious web pages,” in 2013 Fourth International Conference on Digital Manufacturing & Automation, pp. 616–619, IEEE, 2013" }, { "key": "ref17", "doi": "10.1145/1963405.1963436", "unstructured": "D. 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Weippl, “Advanced social engineering attacks,” Journal of Information Security and applications, vol. 22, pp. 113–122, 2015" } ], "record": { "registrant": "Longman Publishers", "registered": "2020-10-29", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/512", "sha256_before": null, "sha256_after": "4c8456f56b6a807d0e0c6b79f7fb59109bb2feacf43cbf2ef99a0bfe9a5ad881" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:41", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" }, { "path": "titles.0.value", "from": "A Survey on Phishing Detection and The Importance of Feature\nSelection In Data Mining Classification Algorithms", "to": "A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2020.v5.i6.pp11-18", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2020.v5.i6.pp11-18" } ], "agents": [ { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2020-09-22", "date_type": "PublicationDate", "online": "2020-09-22" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "issue": "Volume 5", "pages": { "first": "11", "last": "18" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/512", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/512/456", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/512/1526", "purpose": "text-mining", "return_type": "application/xml" } ], "abstract": { "value": ": In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2020-09-22", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1007/978-3-642-33167-1_47", "unstructured": "R. Verma, N. Shashidhar, and N. Hossain, “Detecting phishing emails the natural language way,” in European Symposium on Research in Computer Security, pp. 824–841, Springer, 2012" }, { "key": "ref2", "unstructured": "Apwg, apwg report 2019. https://docs.apwg. org/reports/apwg_trends_report_q1_2019. pdf/" }, { "key": "ref3", "doi": "10.1109/icecta.2017.8252051", "unstructured": "A. Subasi, E. Molah, F. Almkallawi, and T. J. 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