Research on API security vulnerability detection and repair mechanism based on deep learning - Zou, Hong; Zhang, Jiafa; Zeng, Zifeng; Xu, Weijie; Jiang, Jiawei (Edelweiss Applied Science and Technology, 2025)
This paper aims to design a deep learning-based mechanism for detecting and repairing API security vulnerabilities, enabling comprehensive monitoring and automated remediation of API interfaces. The proposed system architecture comprises three main modules: a data acquisition and sensing module for real-time monitoring of API performance indicators, a deep learning module utilizing recurrent neural networks (RNN) to analyze API traffic and identify potential vulnerabilities, and a repair module that develops intelligent repair strategies based on the analysis results. Experimental validation shows that the proposed system significantly outperforms traditional rule-matching and support vector machine (SVM) models in terms of vulnerability detection rate, repair rate, and overall quality assessment, highlighting the effectiveness of deep learning models in API security. The research demonstrates that deep learning approaches can enhance the detection and repair of API vulnerabilities, offering a more effective solution compared to conventional methods. This study provides innovative ideas and methodologies for improving API security, which is crucial for safeguarding applications and systems in an increasingly interconnected digital landscape.