基于小样本学习的源码漏洞检测OA北大核心CSTPCD

Source code vulnerability detection is an important means to discover and localize threats to critical systems.At present,the application of deep learning techniques to source generation vulnerability detection has become a research hotspot.However,due to the lack of source code vulnerability samples,limited data condition resources lead to the poor effect of existing source code vulnerability detection methods in small sample scenarios.In this paper,we propose a source code vulnerability detection method based on few-shot learning,which aims to provide a solution for source code vulnerability detection scenarios with limited sample size.The method in this paper consists of four key components:source code slicing and encoding,meta-learning based dataset processing,vulnerability class vector generation based on dynamic routing algorithms,and vulnerability class vector matching based on neural tensor networks.This paper's method is compared with convolutional neural network,prototype network,and relational network,and the experimental results show that this paper's method outperforms the others in terms of accuracy,and can effectively cope with the problem of sparse vulnerability samples in source code.In the case of 2-way 5-shot and 2-way 10-shot,this paper's method achieves 93.92％and 95.08％accuracy,respectively.