Abstract: Most of the smart contract vulnerability detection methods rely on single mode feature extraction, which leads to the problem of low detection accuracy due to insufficient key feature extraction. This paper proposes a smart contract vulnerability detection method based on multimodal feature fusion. Firstly, the construction of the control flow graph (CFG) is constructed by leveraging the abstract syntax tree (AST) trimmed at the source code layer and the data flow relationship based on the opcode layer, which is imported into the graph attention network (GAT) to extract two types of static features. Secondly, the fuzzing test report generated by echidna, a dynamic detection tool, is used to extract path coverage, state changes and other information to build a graph model, and the dynamic features are extracted by graph neural network (GNN). Finally, the extracted static and dynamic features are fused and input into CNN bilstm att model for vulnerability detection, and relevant experiments are carried out on 47398 smart contracts. Experimental results show that compared with eight mainstream detection methods, such as SmartCheck, Mythril, Oyente, BiGGNN, ASTNN, DRGCN, SVCB and CBGRU, the accuracy, recall and F1 value of this method in reentry vulnerability, timestamp vulnerability, integer overflow vulnerability and Tx.origin vulnerability are increased by 50.26%, 59.54% and 58.40%.

Key words: smart contract, feature fusion, vulnerability detection, graph neural network, graph attention network

摘要： 针对智能合约漏洞检测方法中大多依赖单一模态进行特征提取时存在的关键特征提取不充分导致检测准确率较低的问题，提出了一种基于多模态特征融合的智能合约漏洞检测方法.首先，通过源代码层裁剪的抽象语法树(abstract syntax tree, AST)和操作码层依据的数据流关系分别构建控制流图(control flow graph, CFG)，将其导入图注意力网络(graph attention network, GAT)提取2类静态特征.其次，利用动态检测工具Echidna生成的模糊测试报告提取路径覆盖率、状态变化等信息构建图模型，通过图神经网络(graph neural network, GNN)提取动态特征.最后，对提取的静态和动态特征进行融合并输入CNNBiLSTMATT模型进行漏洞检测，并在47398个智能合约上进行相关实验.实验结果表明，相较于SmartCheck，Mythril，Oyente，BiGGNN，ASTNN，DRGCN，SVCB，CBGRU这8种主流检测方法，该方法对重入漏洞、时间戳漏洞、整数溢出漏洞、Tx.origin漏洞的准确率、召回率、F1值提升了50.26%，59.54%，58.40%.

关键词: 智能合约, 特征融合, 漏洞检测, 图神经网络, 图注意力网络