Detecting source code vulnerabilities aims to protect software systems from attacks by identifying inherent vulnerabilities.  Prior studies often oversimplify the problem into binary classification tasks, which poses challenges for deep learning models to effectively learn diverse vulnerability characteristics.  To address this, the following cybersecurity analysts introduced FGVulDet, a fine-grained vulnerability detector that employs multiple classifiers to discern various vulnerability types:- Shangqing Liu from Nanyang Technological University Wei Ma from Nanyang Technological University Jian Wang from Nanyang Technological University Xiaofei Xie from Singapore Management University Ruitao Feng from Singapore Management University Yang Liu from Nanyang Technological University FGVulDet Vulnerability Detector Each classifier learns type-specific semantics, and researchers propose a novel data augmentation technique to enhance diversity in the training dataset.  Inspired by graph neural networks, FGVulDet utilizes an edge-aware GGNN to capture program semantics from a large-scale GitHub dataset encompassing five vulnerability types. Five Vulnerability Types Previous works have simplified the identification of source code vulnerability into a binary classification problem where all defect-prone functions are labeled as 1.

Source: GBHackers