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October 2, 2025Open Access

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT

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Authors

YTYe TianYZYumin ZhangYJYifan Jia

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Overview

Framework improves detection accuracy for malicious URLs by integrating HTML subgraphs and bidirectional analyses.

Key Points

  • WebGuard++ achieves significant improvements over existing methods, increasing true positive rates by 1.1x-7.9x.
  • The framework incorporates four novel methods to enhance malicious URL detection through improved feature fusion.
  • The Cross-scale URL Encoder effectively learns diverse URL features using transformer networks with dynamic convolution.
  • The Subgraph-aware HTML Encoder amplifies sparse threat signals to better localize malice within HTML structures.

Cite This Study

Tian et al. (2025) studied this question.

synapsesocial.com/papers/68de84bf5b556a9128e1be29https://doi.org/10.48550/arxiv.2506.19356
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection2025
  2. 2Multimodal fusion for malicious URL detection using visual, structural, and semantic representations2026
  3. 3PLG-URLNet: Layer-Wise Fusion of Local Convolution and Global Attention for Detecting Malicious URLs2026
  4. 4NetGuard: A Hybrid Framework for Intelligent and Scalable Malicious URL Detection2026 · 3 citations
  5. 5A study on malicious URL detection based on BiGruCNN-MHA-FTW2026