Abstract
As software vulnerabilities remain prevalent, automatically detecting software vulnerabilities is crucial for software security. Recently neural networks have been shown to be a promising tool in detecting software vulnerabilities. In this paper, we use neural networks trained with program slices, which extract the syntax and semantic characteristics of the source code of programs, to detect software vulnerabilities in C/C++ programs. To achieve a strong prediction model, we combine different types of program slices and optimize different types of neural networks. Our result shows that combining different types of characteristics of source code and using a balanced ratio of vulnerable program slices and non-vulnerable program slices a balanced accuracy in predicting both vulnerable code and non-vulnerable code. Among different neural networks, BGRU performs the best in detecting software vulnerabilities with an accuracy of 94.89%.
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