Extended Abstract of Combine Sliced Joint Graph with Graph Neural Networks for Smart Contract Vulnerability Detection

【Author】 Cai, Jie; Li, Bin; Zhang, Jiale; Sun, Xiaobing; Chen, Bing

【Source】2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING, SANER

【影响因子】

【Abstract】Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible. To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions. Experiment results show that our approach can achieve 89.2% precision and 92.9% recall in smart contract vulnerability detection on our dataset and reveal the effectiveness and efficiency of our approach.

【Keywords】Smart Contract; Vulnerability Detection; Code Representation; Graph Neural Network

【发表时间】2023

【收录时间】2023-07-24

【文献类型】实验仿真

【主题类别】

区块链治理-技术治理-智能合约漏洞检测