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GitHub - COPELONG MEVD [7] Liu Z, Qian P, Wang X, et al Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion [Z] arXiv, 2021 (2021-06-17)
SCVHUNTER: Smart Contract Vulnerability Detection Based on . . . Specifically, SCVHUNTER designs a heterogeneous semantic graph construction phase based on intermediate representations and a vulnerability detection phase based on a heterogeneous graph attention network for smart contracts
Smart Contract Vulnerability Detection Based on Multi Graph . . . To highlight the utilization of grammatical and contextual relationships in smart contract source code and model its intricate structural features, we propose MGCNA, a smart contract vulnerability detection tool based on Multi Graph Convolutional Neural Networks and Self-Attention
EA-RGCN README. md at main · frankdadale EA-RGCN · GitHub Smart contract vulnerability detection based on semantic graph and residual graph convolutional networks with edge attention Usage Unzip utils python-solidity-parser-master zip, and run python3 setup py install Configure the source code path in utils Sourcecode2AST ipynb before running it
VulDet: Smart Contract Vulnerability Detection Based on Graph Attention . . . VulDet: Smart Contract Vulnerability Detection Based on Graph Attention Networks Abstract: Due to the rapid development of blockchain, security issues caused by smart contract vulnerabilities are receiving increasingly widespread attention