Graph Neural Network-based Vulnerability Predication
| Autor: | Weijiang Hong, Chendong Feng, Qi Feng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Graph neural networks business.industry Node (networking) Vulnerability Vulnerability detection 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences 020204 information systems Program graph 0202 electrical engineering electronic engineering information engineering Vulnerability prediction Benchmark (computing) Learning methods Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | ICSME |
| DOI: | 10.1109/icsme46990.2020.00096 |
| Popis: | Automatic vulnerability detection is challenging. In this paper, we report our in-progress work of vulnerability prediction based on graph neural network (GNN). We propose a general GNN-based framework for predicting the vulnerabilities in program functions. We study the different instantiations of the framework in representative program graph representations, initial node encodings, and GNN learning methods. The preliminary experimental results on a representative benchmark indicate that the GNN-based method can improve the accuracy and recall rates of vulnerability prediction. |
| Databáze: | OpenAIRE |
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