Citation

Mushtaq, Shougfta and Javed, Tabassum and Mohd Su'ud, Mazliham Ensemble Learning-Powered URL Phishing Detection: A Performance Driven Approach. Journal of Informatics and Web Engineering, 3 (2). ISSN 2821-370X

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Abstract

With the rapid growth in the usage of the Internet, criminals have found new ways to engage in cyber-attacks. The most common and widespread attack is URL phishing. The proposed system focuses on improving phishing website detection using feature selection and ensemble learning. This model uses two datasets, DS-30 and DS-50, each with 30 and 50 features. Ensemble learning using a voting classifier was then applied to train the model, achieving more accuracy. The combination of HEFS with random forest distribution achieved 94.6% accuracy while minimizing the number of features used (20.8% of the base feature set). The classifier works in the proposed model, and the accuracy is 96% and 98% on the DS-30 and DS-50 datasets, respectively. The hybrid model uses a combination of different factors to distinguish phishing websites from legitimate websites.

Item Type:	Article
Uncontrolled Keywords:	URL Phishing, Phishing Website Detection, Ensemble Learning, Feature Selection, AI-Powered Algorithm, Machine Learning Models
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	11 Jul 2025 00:41
Last Modified:	11 Jul 2025 00:41
URII:	http://shdl.mmu.edu.my/id/eprint/14243

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