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Semantic Aware Fusion Model for Fraudulent Uniform Resource Locator (URL) Classification in the Web Ecosystem

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  • Tang Zhengzheng

    (Zhejiang Normal University, China)

Abstract

The authors propose a Semantic-Aware Fusion Model (SACL). Unlike conventional classification models that rely solely on lexical or statistical features, the approach incorporates semantic relationships extracted from a domain-specific knowledge graph. They conducted single-model validation experiments using 11 popular models, including the Gated Recurrent Unit (GRU) model. Subsequently, they selected three state-of-the-art machine-learning models, including ACmix, demonstrating the highest accuracy in single-model trials for pairwise combinations. They introduce the ACL method to classify fraudulent URLs based on these insights. The experiments show that the Semantic-Aware Fusion Model model surpasses the other selected baselines with a maximum accuracy of 97.6% on the comprehensive dataset. They also validated the SACL model's superior generalizability and practicality through extensive testing.

Suggested Citation

  • Tang Zhengzheng, 2025. "Semantic Aware Fusion Model for Fraudulent Uniform Resource Locator (URL) Classification in the Web Ecosystem," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 21(1), pages 1-20, January.
    • Handle: RePEc:igg:jswis0:v:21:y:2025:i:1:p:1-20
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    References listed on IDEAS

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    1. Liang Zhou & Akshat Gaurav & Varsha Arya & Razaz Waheeb Attar & Shavi Bansal & Ahmed Alhomoud, 2024. "Enhancing Phishing Detection in Semantic Web Systems Using Optimized Deep Learning Models," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-13, January.
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