VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection

• URL: http://arxiv.org/abs/2205.12424v1
• Date: Wed, 25 May 2022 00:56:43 GMT
• Title: VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection
• Authors: Hazim Hanif and Sergio Maffeis
• Abstract summary: VulBERTa is a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. We evaluate our approach on binary and multi-class vulnerability detection tasks across several datasets.
• Score: 1.256413718364189
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. The model learns a deep knowledge representation of the code syntax and semantics, which we leverage to train vulnerability detection classifiers. We evaluate our approach on binary and multi-class vulnerability detection tasks across several datasets (Vuldeepecker, Draper, REVEAL and muVuldeepecker) and benchmarks (CodeXGLUE and D2A). The evaluation results show that VulBERTa achieves state-of-the-art performance and outperforms existing approaches across different datasets, despite its conceptual simplicity, and limited cost in terms of size of training data and number of model parameters.

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