Mobile QR Code QR CODE : The Transactions P of the Korean Institute of Electrical Engineers
The Transactions P of the Korean Institute of Electrical Engineers

Korean Journal of Air-Conditioning and Refrigeration Engineering

ISO Journal TitleTrans. P of KIEE
  • Indexed by
    Korea Citation Index(KCI)

References

1 
Z. Li, D. Zou, S. Xu, X. Ou, H. Jin, S. Wang, Z. Deng, Y. Zhong, 2018, VulDeePecker: A deep learning-based system for vulnerability detection, In Proceedings of the 25th Annual Network and Distributed System Security Symposium (NDSS’18)DOI
2 
CVE DetailsCVE Details Website, 2020, http://https://www.cvedetails.com/Google Search
3 
CWE Website, CWE-119, https://cwe.mitre.org/data/definitions/119.htmlGoogle Search
4 
2020 CWE Top25 Most Dangerous Software Weaknesses, 2020, https://cwe.mitre.org/top25/archive/2020/2020_cwe_top25.htmlGoogle Search
5 
M. Zagane, M. K. Abdi, M. Alenezi, 2020, Deep Learning for Software Vulnerabilities Detection Using Code Metrics, IEEE Access, Vol. 8, pp. 74562-74570DOI
6 
srcML, https://www.srcml.org/Google Search
7 
J. Fan, Y. Li, S. Wang, T. N. Nguyen, 2020, A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries, In MSR ’20: The 17th international Conference on Mining Software Repositories, Vol. acm, pp. 32-41DOI
8 
C. Catal, A. Akbulut, S. Karakatič, M. Pavlinek, V. Podgorelec, 2016, Can we predict software vulnerability with deep neural network? In Proceedings of the 19th International Multiconference Information Society, pp. 19-22Google Search
9 
R. Scandariato, J. Walden, A. Hovsepyan, W. Joosen, 2014, Predicting Vulnerable Software Components via Text Mining, IEEE Trans. on Software Engineering, Vol. 40, No. 10, pp. 993-1006DOI
10 
Z. Li, D, Zou, S. Xu, H. Jin, Y. Zhu, Z. Chen, 2018, SySeVr: A Framework for Using Deep Learning to Detect Software Vulnerabilities, http://arxiv.org/abs/1807.06756DOI
11 
R. Russell, L. Kim, L. Hamilton, T. Lazovich, J. Harer, O. Ozdemir, P. Ellingwood, M. McConley, 2018, Automated vulnerability detection in source code using deep representation learning, In Proceedings of the 17th IEEE International Conference Machine Learning Applications (ICMLA), pp. 757-762DOI
12 
Z. Li, D. Zou, J. Tang, Z. Zhang, M. Sun, H. Jin, 2019, A Comparative Study of Deep Learning-Based Vulnerability Detection System, IEEE Access, Vol. 7, pp. 103184-103197DOI
13 
S. Chakraborty, R. Krishna, Y. Ding, B. Ray, 2020, Deep Learning based Vulnerability Detection: Are We There Yet?, https://arxiv.org/abs/2009.07235DOI
14 
D. Zou, S. Wang, S. Xu, Z. Li, H. Jin, 2019, μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection, IEEE Transactions on Dependable and Secure ComputingDOI
15 
X. Li, L. Wang, Y. Xin, Y. Yang, Y. Chen, 2020, Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning, Applied Sciences, Vol. 10, No. 5DOI