Enhanced Detection of Malicious URLs Using Supervised Machine Learning Models

Title

Enhanced Detection of Malicious URLs Using Supervised Machine Learning Models

Creator

Anu, P.; Saseekala, M.; Subhashini, A.; Aarthee, S.; Kalyani, N.

Description

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.

Source

Communications in Computer and Information Science;Volume;2845 CCIS;pp.407-419

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

F1 score; Feature Extraction; Phishing; Precision; Real-time protection; Recall; URL Detection

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 18650929; ISBN: 978-303220906-1;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-20907-8_34

https://www.scopus.com/pages/publications/105038901940?origin=resultslist