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EDAER: Entropy-Driven Approach for Entity and Relation Extraction in Chinese Cyber Threat Intelligence.

Yong Li1,2, Xiuping Li1,2, Yangbai Zhang1,2

  • 1School of Cybersecurity, Northwestern Polytechnical University, Xi'an 100044, China.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

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This study introduces a new approach for extracting cyber threat intelligence (CTI) from Chinese text, improving accuracy in low-resource scenarios. The entropy-driven approach for entity and relation (EDAER) extraction enhances predictions for cybersecurity threats.

Area of Science:

  • Cybersecurity
  • Natural Language Processing
  • Data Mining

Background:

  • Cyber threat intelligence (CTI) is crucial for system security, transforming raw data into actionable insights.
  • Current CTI research primarily focuses on English, with limited applicability to Chinese due to linguistic differences.
  • Existing Chinese CTI studies struggle with uncertainty in low-resource scenarios.

Purpose of the Study:

  • To enhance Named Entity Recognition (NER) and Relation Extraction (RE) performance in low-resource Chinese CTI.
  • To address the limitations of existing Chinese CTI datasets and extraction methods.
  • To develop a novel approach for more accurate and robust CTI analysis in Chinese.

Main Methods:

  • Construction of a comprehensive Chinese CTI dataset with 16 entity types and 9 relation types.
Keywords:
cross-entropycyber threat intelligenceentity relation extractionentropy regularizationnamed entity recognition

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  • Proposal of an entropy-driven approach for entity and relation (EDAER) extraction, combining RoBERTa_wwm, Mamba, RDCNN, and CRF.
  • Integration of entropy for uncertainty quantification and contrastive learning for feature enhancement in NER and RE tasks.

    • Main Results:

    • The proposed EDAER approach significantly outperforms existing methods on both NER and RE tasks.
    • RoBERTa_wwm demonstrates superior performance over BERT for Chinese CTI NER and RE.
    • Mamba shows improved performance compared to BiLSTM in NER tasks.
    • Entropy-based dynamic gating and uncertainty-guided contrastive learning contribute to performance gains.

    Conclusions:

    • The developed Chinese CTI dataset and EDAER approach offer significant improvements for low-resource CTI analysis.
    • The findings highlight the effectiveness of advanced NLP techniques and uncertainty quantification in enhancing CTI extraction.
    • This work provides a valuable resource and methodology for advancing Chinese CTI research and applications.