Cross-language binary-source code matching with intermediate representations

Yi GUI, Yao WAN, Hongyu ZHANG, Huifang HUANG, Yulei SUI, Guandong XU, Zhiyuan SHAO, Hai JIN

Research output: Chapter in Book/Report/Conference proceedingChapters

8 Citations (Scopus)

Abstract

Binary- source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks. Copyright © 2022 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022
Place of PublicationUSA
PublisherIEEE
Pages601-612
ISBN (Electronic)9781665437868
DOIs
Publication statusPublished - 2022

Citation

Gui, Y., Wan, Y., Zhang, H., Huang, H., Sui, Y., Xu, G., Shao, Z., & Jin, H. (2022). Cross-language binary-source code matching with intermediate representations. In Proceedings of 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022 (pp. 601-612). IEEE. https://doi.org/10.1109/SANER53432.2022.00077

Keywords

  • Cross-language
  • Clone detection
  • Intermediate representation
  • Binary code
  • Code matching
  • Deep learning

Fingerprint

Dive into the research topics of 'Cross-language binary-source code matching with intermediate representations'. Together they form a unique fingerprint.