Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Software Vulnerability Detection Method Based on Abstract Syntax Tree Feature Migration(AST-FMVD)

Version 1 : Received: 5 September 2023 / Approved: 6 September 2023 / Online: 6 September 2023 (10:20:03 CEST)

How to cite: Li, Z.-J.; Li, T.; Chen, H.-D.; Yu, Q.; Qiao, M.-Q.; Li, L. Software Vulnerability Detection Method Based on Abstract Syntax Tree Feature Migration(AST-FMVD). Preprints 2023, 2023090374. https://doi.org/10.20944/preprints202309.0374.v1 Li, Z.-J.; Li, T.; Chen, H.-D.; Yu, Q.; Qiao, M.-Q.; Li, L. Software Vulnerability Detection Method Based on Abstract Syntax Tree Feature Migration(AST-FMVD). Preprints 2023, 2023090374. https://doi.org/10.20944/preprints202309.0374.v1

Abstract

In the broad context of vulnerability detection, deep learning has achieved considerable progress but faces generalization challenges in multilingual environments . We introduce a novel approach named AST-FMVD, which leverages transfer learning and abstract syntax trees. By employing semantic similarity clustering and context-aware technology, the method constructs node mapping relationships between different languages, enabling zero-shot learning in vulnerability detection. The method was validated by applying Java's vulnerability detection model in the Python domain, successfully demonstrating that AST-FMVD retains the original model's detection capabilities in the target domain. In conclusion, the proposed method offers a promising solution to the inherent problems in multi-language vulnerability detection, signifying a potential leap in the application of deep learning, transfer learning, and abstract syntax trees for improved cross-domain performance.

Keywords

deep learning; transfer learning; zero-shot; vulnerability detection; abstract syntax tree

Subject

Computer Science and Mathematics, Security Systems

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