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

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Titel: MSSA: multi-stage semantic-aware neural network for binary code similarity detection.
Autoren: Wan, Bangrui, Zhou, Jianjun, Wang, Ying, Chen, Feng, Qian, Ying
Quelle: PeerJ Computer Science; Jan2025, p1-22, 22p
Schlagwörter: BINARY codes, INTEGRAL functions, TRANSFORMER models, LINEAR network coding, DEEP learning
Abstract: Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. Current state-of-the-art approaches are based on Transformer, which require substantial computation resources. Learning-based approaches remains room for optimization in learning the deeper semantics of binary code. In this paper, we propose MSSA, a multi-stage semantic-aware neural network for BCSD at the function level. It effectively integrates the semantic and structural information of assembly instructions within and between basic blocks, and across the entire function through four semantic-aware neural networks, achieving deep understanding of binary code semantics. MSSA is a lightweight model with only 0.38M parameters in its backbone network, suitable for deployment in CPU environments. Experimental results show that MSSA outperforms Gemini, Asm2Vec, SAFE, and jTrans in classification performance and ranks second only to the Transformer-based jTrans in retrieval performance. [ABSTRACT FROM AUTHOR]
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  Data: MSSA: multi-stage semantic-aware neural network for binary code similarity detection.
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  Data: <searchLink fieldCode="AR" term="%22Wan%2C+Bangrui%22">Wan, Bangrui</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Jianjun%22">Zhou, Jianjun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Ying%22">Wang, Ying</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Feng%22">Chen, Feng</searchLink><br /><searchLink fieldCode="AR" term="%22Qian%2C+Ying%22">Qian, Ying</searchLink>
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  Data: PeerJ Computer Science; Jan2025, p1-22, 22p
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  Data: Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. Current state-of-the-art approaches are based on Transformer, which require substantial computation resources. Learning-based approaches remains room for optimization in learning the deeper semantics of binary code. In this paper, we propose MSSA, a multi-stage semantic-aware neural network for BCSD at the function level. It effectively integrates the semantic and structural information of assembly instructions within and between basic blocks, and across the entire function through four semantic-aware neural networks, achieving deep understanding of binary code semantics. MSSA is a lightweight model with only 0.38M parameters in its backbone network, suitable for deployment in CPU environments. Experimental results show that MSSA outperforms Gemini, Asm2Vec, SAFE, and jTrans in classification performance and ranks second only to the Transformer-based jTrans in retrieval performance. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of PeerJ Computer Science is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.7717/peerj-cs.2504
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        Text: English
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        Type: general
      – SubjectFull: INTEGRAL functions
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      – SubjectFull: LINEAR network coding
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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