EFFICIENT DEEP FEATURES LEARNING FOR VULNERABILITY DETECTION USING CHARACTER N- GRAM EMBEDDING - Mamdouh Alenezi; Mohammed Zagane; Yasir Javed (Jordanian Journal of Computers and Information Technology, 2021)
Deep Learning (DL) techniques were successfully applied to solve challenging problems in the field of Natural Language Processing (NLP). Since source code and natural text share several similarities, it was possible to adopt text classification techniques, such as word embedding, to propose DL-based Automatic Vulnerabilities Prediction (AVP) approaches. Although the obtained results were interesting, they were not good enough compared to those obtained in NLP. In this paper, we propose an improved DL-based AVP approach based on the technique of character n-gram embedding. We evaluate the proposed approach for 4 types of vulnerabilities using a large c/c++ open-source codebase. The results show that our approach can yield a very excellent performance which outperforms the performances obtained by previous approaches.