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Digital Forensics and Cyber Crime. 11th EAI International Conference, ICDF2C 2020, Boston, MA, USA, October 15-16, 2020, Proceedings

Research Article

Neural Representation Learning Based Binary Code Authorship Attribution

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  • @INPROCEEDINGS{10.1007/978-3-030-68734-2_15,
        author={Zhongmin Wang and Zhen Feng and Zhenzhou Tian},
        title={Neural Representation Learning Based Binary Code Authorship Attribution},
        proceedings={Digital Forensics and Cyber Crime. 11th EAI International Conference, ICDF2C 2020, Boston, MA, USA, October 15-16, 2020, Proceedings},
        proceedings_a={ICDF2C},
        year={2021},
        month={2},
        keywords={Authorship attribution Binary code Neural network Representation learning},
        doi={10.1007/978-3-030-68734-2_15}
    }
    
  • Zhongmin Wang
    Zhen Feng
    Zhenzhou Tian
    Year: 2021
    Neural Representation Learning Based Binary Code Authorship Attribution
    ICDF2C
    Springer
    DOI: 10.1007/978-3-030-68734-2_15
Zhongmin Wang, Zhen Feng, Zhenzhou Tian,*
    *Contact email: tianzhenzhou@xupt.edu.cn

    Abstract

    Authorship attribution on binary code is of great value in applications such as malware analysis, software forensics, and code theft detection. Inspired by the recent great successes of neural network and representation learning in various program analysis tasks, this study proposes NMPI to achieve fine-grained program authorship attribution by analyzing the binary codes of individual functions from the perspective of sequence and structural. To evaluate the NMPI, the study constructs a large dataset consisting of 268796 functions collected from Google CodeJam. The extensive experimental evaluation shows that NMPI can achieve 91% accuracy for the function-level binary code authorship attribution task.

    Keywords
    Authorship attribution Binary code Neural network Representation learning
    Published
    2021-02-07
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-030-68734-2_15
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