NDSS 2018 - VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Session 3A: Deep Learning and Adversarial ML - 02 VulDeePecker: A Deep Learning-Based System for Vulnerability Detection SUMMARY The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning.