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GBsim: A Robust GCN-BERT Approach for Cross-Architecture Binary Code Similarity Analysis.

Jiang Du1, Qiang Wei1, Yisen Wang1

  • 1School of Cyber Science and Engineering, Information Engineering University, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

GBsim enhances binary code similarity detection by combining graph neural networks and natural language processing. This approach improves vulnerability identification accuracy across different architectures, even with noisy data.

Keywords:
binary code similarity analysiscross-architecture embeddinggraph neural network robustnesshybrid deep learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Graph neural networks (GNNs) excel at structural pattern learning but struggle with noisy graph construction and cross-architecture variations in binary code analysis.
  • Existing GNNs degrade significantly in low signal-to-noise ratio environments and cross-domain settings, impacting critical security tasks like vulnerability identification.

Purpose of the Study:

  • To develop a robust method for binary code similarity detection and vulnerability identification that overcomes noise and cross-architecture distribution shifts.
  • To enhance the performance of GNNs in mission-critical security applications by integrating natural language processing techniques.

Main Methods:

  • Proposed GBsim, a novel approach combining GNNs and NLP.
  • Utilized a cross-architecture language model to convert binary functions into semantic graphs.
  • Employed a multilayer GCN for structural feature extraction and a Transformer layer for semantic information integration.

Main Results:

  • GBsim achieved a Mean Reciprocal Rank (MRR) of 0.901 and Recall@1 of 0.831 on a large-scale cross-architecture dataset, surpassing state-of-the-art methods.
  • In real-world vulnerability detection, GBsim demonstrated an 81.3% average recall rate on a 1-day vulnerability dataset, outperforming existing methods by 2.1%.

Conclusions:

  • GBsim generates robust cross-architecture embeddings, maintaining high performance despite significant distribution shifts and noisy graph data.
  • The method effectively preserves information across architectural boundaries, enhancing model robustness for practical security-sensitive vulnerability identification tasks.