Abstract
The rapid development of software has brought unprecedented severe challenges to software security vulnerabilities. Traditional vulnerability mining methods are difficult to apply to large-scale software systems due to drawbacks such as manual inspection, low efficiency, high false positives and high false negatives. Recent research works have attempted to apply deep learning models to vulnerability mining, and have made a good progress in vulnerability mining filed. In this paper, we analyze the deep learning model framework applied to vulnerability mining and summarize its overall workflow and technology. Then, we give a detailed analysis on five feature extraction methods for vulnerability mining, including sequence characterization-based method, abstract syntax tree-based method, graph-based method, text-based method and mixed characterization-based method. In addition, we summarize their advantages and disadvantages from the angles of single and mixed feature extraction method. Finally, we point out the future research trends and prospects.
This work was supported by the Key Research and Development Science and Technology of Hainan Province(ZDYF202012), the National Key Research and Development Program of China(2018YFB0804701), and the National Natural Science Foundation of China (U1836210).
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Li, Y. et al. (2022). A Review of Data Representation Methods for Vulnerability Mining Using Deep Learning. In: Cao, C., Zhang, Y., Hong, Y., Wang, D. (eds) Frontiers in Cyber Security. FCS 2021. Communications in Computer and Information Science, vol 1558. Springer, Singapore. https://doi.org/10.1007/978-981-19-0523-0_22
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