• Corpus ID: 227127587

Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned

@article{Kim2020RevisitingBC,
  title={Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned},
  author={Dongkwan Kim and Eunsoo Kim and Sang Kil Cha and Sooel Son and Yongdae Kim},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.10749}
}
Binary code similarity analysis (BCSA) is widely used for diverse security applications such as plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize… 
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