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VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs by Yuanhai Fan; Chuanhao Wan; Cai Fu; Lansheng Han; Hao Xu is a Computer Science article available to read on EtoBox.

What is VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs about?

Code vulnerability detection has long been a critical issue due to its potential threat to computer systems. It is imperative to detect source code vulnerabilities in software and remediate them to avoid cyber attacks. To automate detection and reduce labor costs, many deep learning-based methods have been proposed. However, these approaches have been found to be either ineffective in detecting multiple classes of vulnerabilities or limited by treating original source code as a natural language sequence without exploiting the structural information of code. In this paper, we propose VDoTR, a model that leverages a new tensor representation of comprehensive code graphs, including AST, CFG, DFG, and NCS, to detect multiple types of vulnerabilities. Firstly, a tensor structure is introduced to represent the structured information of code, which deeply captures code features. Secondly, a new Circle Gated Graph Neural Network(CircleGGNN) is designed based on tensor for hidden state embedding of nodes. CircleGGNN can perform heterogeneous graph information fusion more directly and effectively. Lastly, a 1-D convolution-based output layer is applied to hidden embedding features for classi

Who reads VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs?

Author

Yuanhai Fan; Chuanhao Wan; Cai Fu; Lansheng Han; Hao Xu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)