Objective: To address the limitations of existing deep learning-based vulnerability detection approaches, we propose BGNN4VD (Bidirectional Graph Neural Network ...
Objective: To address the limitations of existing deep learning-based vulnerability detection approaches, we propose BGNN4VD (Bidirectional Graph Neural Network ...
In this work, we propose to construct a Bidirectional GraphNeural-Network (BGNN) by introducing backward edges foraccommodating more information related to ...
Mar 20, 2021 · Objective: To address the limitations of existing deep learning-based vulnerability detection approaches, we propose BGNN4VD (Bidirectional ...
Oct 22, 2024 · Objective To address the limitations of existing vulnerability detection approaches, we propose BGNN4VD (Bidirectional Graph Neural Network for ...
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BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection · journal article · research article · Published by Elsevier in Information ...
CAG combines the principles of different code analyses such as abstract syntax tree, control flow graph, and program dependence graph with dominator and ...
BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection ... Zhuang, Smart contract vulnerability detection using graph neural network ...
Bgnn4vd: Constructing bidirectional graph neural-network for vulnerability detection · MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph ...
Graph neural networks (GNNs) have proven to be an effective tool for vulnerability discovery that outperforms learning-based methods working directly on ...