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DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network

Canan Batur Sahin. DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network. In International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Turkey, August 25-27, 2021. pages 1-8, IEEE, 2021. [doi]

@inproceedings{Sahin21-2,
  title = {DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network},
  author = {Canan Batur Sahin},
  year = {2021},
  doi = {10.1109/INISTA52262.2021.9548609},
  url = {https://doi.org/10.1109/INISTA52262.2021.9548609},
  researchr = {https://researchr.org/publication/Sahin21-2},
  cites = {0},
  citedby = {0},
  pages = {1-8},
  booktitle = {International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Turkey, August 25-27, 2021},
  publisher = {IEEE},
  isbn = {978-1-6654-3603-8},
}
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