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Multi-semantic feature fusion attention network for binary code similarity detection.

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  • 1Department of Security Technology, China Mobile Research Institute, Beijing, 100053, China. Banglingliyjy@163.com.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Binary code similarity detection (BCSD) is crucial for software security testing, including plagiarism detection, malware analysis, and vulnerability identification.
  • Existing methods using recurrent neural networks struggle with long-distance semantic information and lack in-depth fusion of low-level and high-level features.

Purpose of the Study:

  • To propose a novel Multi-Semantic Feature Fusion Attention Network (MFFA-Net) for enhanced BCSD.
  • To address the limitations of current BCSD approaches in capturing comprehensive semantic information.

Main Methods:

  • Developed MFFA-Net with two key modules: Semantic Feature Fusion (SFF) and Attention Feature Fusion (AFF).
  • The SFF module concatenates diverse semantic features for a holistic function representation.
  • The AFF module employs an attention matrix to identify and weigh relevant features, exploring inter-feature relationships.

Main Results:

  • MFFA-Net achieved high Area Under the Curve (AUC) scores of 99.6% and 98.3% on two independent datasets.
  • Experimental results demonstrate superior performance of MFFA-Net in binary code similarity detection tasks.

Conclusions:

  • MFFA-Net effectively overcomes the limitations of previous methods by integrating multi-level semantic features.
  • The proposed network offers a significant advancement in the field of binary code similarity detection, enhancing its applicability in software security.