JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning

Shengquan Chen; Nan Jiang; Zheng Wu; Zichen Wang

doi:10.1117/12.2667324

28 March 2023 JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning

Shengquan Chen, Nan Jiang, Zheng Wu, Zichen Wang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256645 (2023) https://doi.org/10.1117/12.2667324
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Software vulnerabilities will make the system vulnerable to attack, affect the reliability of the software and cause information leakage, which will have a huge impact on enterprises or individuals. Vulnerabilities are inevitable in software development engineering. Therefore, relying on some methods or tools for continuous vulnerability analysis of code is the solution to minimize software vulnerabilities. We propose a neural network model, JSVulExplorer, for static vulnerability analysis of the dynamic programming language JavaScript. The JSVulExplorer focuses on feature enhancement of data. We use pre-training to learn the semantic similarity between code slices, utilize abstract syntax trees to generate path information, and design positional encoding to use the path information. Based on transfer learning, we combine the pre-trained model with path information to improve vulnerability detection performance. Experiments show that JSVulExplorer has significantly improved precision and recall compared to previous models. It is verified that the dynamic event-based programming language can also use the static analysis method for vulnerability detection.

Citation Download Citation

Shengquan Chen, Nan Jiang, Zheng Wu, and Zichen Wang "JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256645 (28 March 2023); https://doi.org/10.1117/12.2667324

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available