Vulnerability Detection for Source Code Using Contextual LSTM
| Title | Vulnerability Detection for Source Code Using Contextual LSTM |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Xu, A., Dai, T., Chen, H., Ming, Z., Li, W. |
| Conference Name | 2018 5th International Conference on Systems and Informatics (ICSAI) |
| ISBN Number | 978-1-7281-0120-0 |
| Keywords | CLSTM, component, compositionality, Conferences, Human Behavior, Informatics, Metrics, Neural Network, pubcrawl, Resiliency, vulnerability detection |
| Abstract | With the development of Internet technology, software vulnerabilities have become a major threat to current computer security. In this work, we propose the vulnerability detection for source code using Contextual LSTM. Compared with CNN and LSTM, we evaluated the CLSTM on 23185 programs, which are collected from SARD. We extracted the features through the program slicing. Based on the features, we used the natural language processing to analysis programs with source code. The experimental results demonstrate that CLSTM has the best performance for vulnerability detection, reaching the accuracy of 96.711% and the F1 score of 0.96984. |
| URL | https://ieeexplore.ieee.org/document/8599360 |
| DOI | 10.1109/ICSAI.2018.8599360 |
| Citation Key | xu_vulnerability_2018 |