Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anu, P.; Saseekala, M.; Subhashini, A.; Aarthee, S.; Kalyani, N. Title A name given to the resource Enhanced Detection of Malicious URLs Using Supervised Machine Learning Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2845 CCIS;pp.407-419 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-20907-8_34" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-20907-8_34</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038901940?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038901940?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anu P., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Saseekala M., Faculty of Computer Applications, School of Business and Management, CHRIST University, Bangalore, India; Subhashini A., Department of Software Systems, PSG College of Arts and Science, Coimbatore, 641014, India; Aarthee S., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Kalyani N., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India Description An account of the resource This paper deliberates on URL phishing, one important subset of cyber threats. Most modern-day deceptive practices have shifted to the digital space due to the vast scope of information available on the internet. URL phishing is a dishonest practice that includes masquerading harmful links as legitimate links to trick users into sharing their private data. Detection of URL phishing is extremely challenging, hence most of these attacks go undetected until it is too late for the victim. Automatic blacklist that rely heavily on user-generated reports to monitor internet links have been repeatedly proven ineffective time and again. Along with failing to identify newly listed phishing sites, these systems also tend to mistake harmless links for phishing traps. This paper proposes the application of classification techniques of practical machine learning, specifically analysing the patterns and behaviours of URLs to detect phishing websites accurately. Leveraging the properties of Decision Trees, Random Forests, Logistic Regression, SVM, and Light GBM, we were able to come up with a detection model, which precisely calculates accuracy, precision, recall, as well as F1 score to evaluate the validity of URL classification. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource F1 score; Feature Extraction; Phishing; Precision; Real-time protection; Recall; URL Detection Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303220906-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online