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Cross-platform binary code similarity detection based on NMT and graph embedding.

Xiaodong Zhu1, Liehui Jiang1, Zeng Chen2

  • 1State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China.

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Summary

This study introduces a novel cross-platform binary code similarity detection method using neural machine translation and graph embedding. The SimInspector prototype achieves higher accuracy than existing approaches for identifying similar binary functions.

Keywords:
binary code similarity detectiondeep learninggraph embeddingneural machine translation

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Cross-platform binary code similarity detection is crucial for various applications.
  • Traditional methods (CFG matching) lack efficiency and scalability.
  • Existing deep learning methods suffer from low accuracy and reliance on manual features.

Purpose of the Study:

  • To propose an efficient and accurate cross-platform binary code similarity detection method.
  • To overcome limitations of traditional and existing deep learning approaches.
  • To automatically extract semantic features for improved similarity detection.

Main Methods:

  • Utilizing neural machine translation (NMT) for semantic feature extraction.
  • Employing graph embedding techniques to represent binary code semantics.
  • Measuring similarity via the distance between high-dimension embedding vectors.
  • Developing a prototype system named SimInspector.

Main Results:

  • SimInspector demonstrates superior performance compared to the state-of-the-art (Gemini).
  • Achieved approximately 6% higher accuracy in similarity detection.
  • Maintained good efficiency in detecting binary code similarity.

Conclusions:

  • The proposed NMT and graph embedding method effectively enhances cross-platform binary code similarity detection.
  • SimInspector offers a promising solution for accurate and efficient binary similarity analysis.
  • This approach advances the field by automating feature extraction and improving detection accuracy.