@inproceedings{
wang2024suitable,
title={Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection},
author={Jingjing Wang and Minhuan Huang and Yuanping Nie and Xiang Li and Qianjin Du and Wei Kong and Huan Deng and Xiaohui Kuang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OP2D9sIdo4}
}
Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth.
Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection
Jingjing Wang Minhuan Huang Yuanping Nie Xiang Li Qianjin Du Wei Kong Huan Deng Xiaohui Kuang