Vulnerability Detection for Source Code Using Contextual LSTM | IEEE Conference Publication | IEEE Xplore

Vulnerability Detection for Source Code Using Contextual LSTM


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 ...Show More

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.
Date of Conference: 10-12 November 2018
Date Added to IEEE Xplore: 03 January 2019
ISBN Information:
Conference Location: Nanjing, China

References

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