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Binary code similarity analysis based on naming function and common vector space.

Bing Xia1,2, Jianmin Pang3, Xin Zhou4

  • 1State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China. xiabing@zut.edu.cn.

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Summary
This summary is machine-generated.

N_Match improves cross-platform binary code similarity analysis by extracting stable, platform-independent semantics. This enhances vulnerability search accuracy, outperforming existing models in identifying similar computations across different platforms.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Binary code similarity analysis is crucial for vulnerability search when source code is unavailable.
  • Existing cross-platform methods struggle with instruction syntax differences and aligning control flow graphs.
  • High-level, stable semantics are underexplored for robust cross-platform binary function comparison.

Purpose of the Study:

  • To propose a novel cross-platform binary function similarity comparison model, N_Match.
  • To address limitations of existing methods in handling platform-specific instruction variations and semantic alignment.
  • To improve the accuracy of identifying similar computations in binary functions across different platforms and optimization levels.

Main Methods:

  • Developed N_Match, a model that maps diverse platform instructions into a unified semantic space.
  • Utilized graph embedding to learn stable neighbor semantics and incorporated naming function knowledge for high-level semantics.
  • Combined stable graph structures with platform-independent API knowledge for final function semantic representation.

Main Results:

  • N_Match demonstrated superior accuracy over baseline models in cross-platform, cross-optimization, and industrial scenarios.
  • Vulnerability search experiments showed significant improvements in hit@N with N_Match.
  • The mean average precision (mAP) achieved by N_Match exceeded current graph embedding models by 66%.

Conclusions:

  • Extracting stable, platform-independent semantics is key to enhancing binary code similarity analysis accuracy.
  • N_Match effectively overcomes cross-platform challenges by unifying instruction semantics and leveraging high-level knowledge.
  • The proposed model offers significant advancements for vulnerability search and binary analysis.