Detecting Software Vulnerabilities Based on Hierarchical Graph Attention Network
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9119-8_11
Reference28 articles.
1. Hin, D., Kan, A., Chen, H., Babar, M. A.: LineVD: statement-level vulnerability detection using graph neural networks. In: Proceedings of the 19th International Conference on Mining Software Repositories, pp. 596–607. ACM, Pittsburgh, PA, USA (2022)
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3. Kronjee, J., Hommersom, A., Vranken, H.: Discovering software vulnerabilities using data-flow analysis and machine learning. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, pp. 6:1–6:10. Springer, Hamburg (2018)
4. Grieco, G., Grinblat, G. L., Uzal, L., Rawat, S., Feist, J., Mounier, L.: Toward Large-scale vulnerability discovery using machine learning. In: Proceedings of the Sixth ACM on Conference on Data and Application Security and Privacy, pp. 85–96. New Orleans, LA, USA (2016)
5. Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019)
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