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6 June 2024 Low-code vulnerability identification based on TextCNN
Yuqiong Wang, Yuxiao Zhao, Xiang Wang, Weidong Tang, Jinhui Zhang, Zhaojie Yang, Peng Wang, Jian Hu
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Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 131750F (2024) https://doi.org/10.1117/12.3031890
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China
Abstract
Vulnerability identification is a crucial quality assurance step in software engineering, dedicated to discovering and handling potential errors and abnormal behavior in source code. Most vulnerability detection methods are designed for conventional programming languages. With the widespread adoption of low-code development, there is a need for a vulnerability detection method specifically tailored to low-code environments. Thus, we present a robust low-code vulnerability identification model by integrating Convolutional Neural Network Text Classification (TextCNN) and an attention mechanism. The resulting model is capable of recognizing potential irregular patterns in the low code, assisting developers in promptly identifying and addressing potential software defects. It holds significant importance in enhancing the maintainability, stability, and security of the system. Simultaneously, it offers substantial support for the company's software development efforts and mitigates the risk of software defects. The experimental results demonstrate that the method in this paper can achieve accurate low-code vulnerability identification.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Yuqiong Wang, Yuxiao Zhao, Xiang Wang, Weidong Tang, Jinhui Zhang, Zhaojie Yang, Peng Wang, and Jian Hu "Low-code vulnerability identification based on TextCNN", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 131750F (6 June 2024); https://doi.org/10.1117/12.3031890
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KEYWORDS
Deep learning
Network security
Feature extraction
Software development
Convolution
Data modeling
Semantics
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