Smart Contract Vulnerability Detection based on Heterogeneous Graph
To address that the existing smart contract vulnerability detection-based deep learning cannot effectively use context information,This paper proposes a smart contract vulnerability detection based on a heterogeneous graph,Which parses the contract into a Symbol diagram containing data-flow edge and control-flow edge.Then it uses graph neural net-works to perform representation learning on the graph,finally,the vulnerability prediction is performed through the neural networks.Experiments are conducted on ESC and VSC data sets,and comparing them with existing tools and models,the results show that the method has improved in the four indicators of accuracy,recall,precision,and F1-score.
万方数据
维普
