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Due to its powerful automatic feature extraction, deep learning (DL) has beenwidely used in source code vulnerability detection. However, although itperforms well on artificial datasets, its performance is not satisfactory whendetecting real-world vulnerabilities due to the high complexity of real-worldsamples. In this paper, we propose to train DL-based vulnerability detectionmodels in a human-learning manner, that is, start with the simplest samples andthen gradually transition to difficult knowledge. Specifically, we design anovel framework (Humer) that can enhance the detection ability of DL-basedvulnerability detectors. To validate the effectiveness of Humer, we select fivestate-of-the-art DL-based vulnerability detection models (TokenCNN,VulDeePecker, StatementGRU, ASTGRU, and Devign) to complete our evaluations.Through the results, we find that the use of Humer can increase the F1 of thesemodels by an average of 10.5%. Moreover, Humer can make the model detect up to16.7% more real-world vulnerabilities. Meanwhile, we also conduct a case studyto uncover vulnerabilities from real-world open source products by using theseenhanced DL-based vulnerability detectors. Through the results, we finallydiscover 281 unreported vulnerabilities in NVD, of which 98 have been silentlypatched by vendors in the latest version of corresponding products, but 159still exist in the products.

Boosting the Capability of Intelligent Vulnerability Detection by Training in a Human-Learning Manner