Abstract :

The Rapid Growth Of Internet Services Has Led To An Increase In Malicious Websites That Aim To Steal Sensitive Information Through Phishing, Malware Distribution, And Fraudulent Activities. Traditional Blacklist-based Approaches Fail To Detect Zero-day Attacks And Newly Generated Malicious URLs. This Research Proposes A Realtime Malicious Website Classification Framework Using Supervised Machine Learning Models Including Support Vector Machine (SVM), Logistic Regression, Naïve Bayes Classifier, And Gradient Boosting Classifier. A Benchmark Dataset Consisting Of URL-based Lexical And Domain Features Is Utilized. The Dataset Is Preprocessed And Divided Into Training And Testing Sets. Multiple Models Are Trained And Evaluated Using Performance Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That The Gradient Boosting Classifier Outperforms Other Algorithms, Achieving Superior Accuracy And Robustness In Detecting Malicious Websites In Real Time.

Published:

27-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

227-232

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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