Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks

Abstract

Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.