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MSSA: multi-stage semantic-aware neural network for binary code similarity detection.

Bangrui Wan1,2, Jianjun Zhou1, Ying Wang1

  • 1School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.

Peerj. Computer Science
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MSSA, a lightweight neural network for binary code similarity detection. MSSA effectively identifies similar code functions, outperforming existing methods in classification tasks.

Keywords:
Binary analysisNeural networkSimilarity detection

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Binary code similarity detection (BCSD) is crucial for malware analysis, patch analysis, and clone detection.
  • Existing Transformer-based methods for BCSD demand significant computational resources.
  • Current learning-based approaches have limitations in capturing deep binary code semantics.

Purpose of the Study:

  • To propose MSSA, a novel multi-stage semantic-aware neural network for function-level BCSD.
  • To develop a lightweight model suitable for CPU environments.
  • To enhance the understanding of deep binary code semantics.

Main Methods:

  • MSSA integrates semantic and structural information of assembly instructions.
  • The model utilizes four semantic-aware neural networks for comprehensive analysis.
  • It processes information within and between basic blocks, and across entire functions.

Main Results:

  • MSSA demonstrates superior classification performance compared to Gemini, Asm2Vec, SAFE, and jTrans.
  • In retrieval performance, MSSA ranks second only to the Transformer-based jTrans.
  • The proposed model is lightweight, with only 0.38M parameters in its backbone network.

Conclusions:

  • MSSA offers an effective and efficient solution for binary code similarity detection.
  • The model's lightweight nature makes it suitable for practical deployment.
  • MSSA advances the field by enabling deeper semantic understanding of binary code.