VDGraph2Vec: Vulnerability Detection in Assembly Code Using Message Passing Neural Networks
Published in ICMLA 2022, 2022
Recommended citation: Ashita Diwan, Miles Q. Li, and Benjamin CM Fung. "VDGraph2Vec: Vulnerability Detection in Assembly Code Using Message Passing Neural Networks." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1039-1046. IEEE, 2022. https://ieeexplore.ieee.org/document/10069134/
This paper proposes VDGraph2Vec, an automated deep learning method to generate representations of assembly code for the task of vulnerability detection. It embeds the control flow and semantic information of assembly code using the expressive capabilities of message passing neural networks and RoBERTa.
Recommended citation: Ashita Diwan, Miles Q. Li, and Benjamin CM Fung. “VDGraph2Vec: Vulnerability Detection in Assembly Code Using Message Passing Neural Networks.” In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1039-1046. IEEE, 2022.