Based Vulnerability Detection

Based vulnerability detection aims to automatically identify security flaws in software code using machine learning, addressing the limitations of traditional manual methods. Current research heavily utilizes deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and graph neural networks (GNNs), often combined with techniques like data augmentation and ensemble learning to improve accuracy and address data scarcity. These advancements are crucial for enhancing software security by enabling faster and more efficient vulnerability identification, though challenges remain in achieving high precision and generalizability across diverse codebases and vulnerability types.