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Related Experiment Videos

Multimodal fusion for malicious URL detection using visual, structural, and semantic representations.

Junhyeong Lee1, Hyun Kwon2

  • 1Department of Artificial Intelligence and Data Science, Korea Military Academy, Seoul, 01805, South Korea.

Scientific Reports
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a multimodal framework for URL classification, integrating visual, structural, and semantic data. The novel approach significantly improves accuracy in identifying malicious URLs.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Data Science

Background:

  • URL classification is crucial for cybersecurity.
  • Existing methods often rely on single data modalities, limiting accuracy.
  • Malicious URLs exhibit complex patterns across visual, structural, and semantic features.

Purpose of the Study:

  • To develop a novel multimodal framework for enhanced URL classification accuracy.
  • To systematically integrate visual, structural, and semantic representations.
  • To outperform traditional single-modal and ensemble methods.

Main Methods:

  • URLs transformed into grayscale images and processed via Convolutional Neural Network (CNN) with ResNet architecture.
  • Structural features extracted and analyzed using XGBoost and CatBoost classifiers.
Keywords:
CatBoostDistilBERTMalicious URL detectionMultimodal learningXGBoost

Related Experiment Videos

  • Semantic insights derived from fine-tuned DistilBERT model embeddings.
  • Fusion of visual, structural, and semantic outputs via a fully connected Deep Neural Network (DNN).

    • Main Results:

    • The multimodal fusion framework achieved a superior F1-score of 0.9189.
    • The framework demonstrated a high Area Under the Curve (AUC) of 0.9805.
    • Significant performance improvement over single-modal and existing ensemble baselines was observed.

    Conclusions:

    • The proposed multimodal framework effectively integrates diverse data representations for URL classification.
    • This approach offers a significant advancement in detecting malicious URLs.
    • The findings highlight the power of multimodal learning in cybersecurity applications.