通信学报2024，Vol.45Issue(9)：101-114,14.DOI:10.11959/j.issn.1000-436x.2024163
基于预训练与新型时序图神经网络的智能合约漏洞检测方法
Smart contract vulnerability detection method based on pre-training and novel timing graph neural network
摘要
Abstract
To address the limitations of current deep learning-based methods in extracting contract bytecode features and representing vulnerability semantics,as well as the shortcomings of the traditional graph neural networks in learning tem-poral information from contract statements,a method for detecting vulnerabilities in contracts was proposed based on pre-trained and temporal graph neural network.Firstly,the pre-trained model was used to transform smart contract byte-code into a vulnerability semantics-aware contract graph structure.Then,combined with a self-attention mechanism,the event-driven temporal graph neural network was designed to extract temporal information during contract execution.Fi-nally,focusing on reentrant vulnerabilities,timestamp dependency vulnerabilities,and Tx.origin authentication vulner-abilities,extensive experiments were conducted on a dataset of 120 932 actual contracts.The results show that the pro-posed method significantly outperforms existing approaches.关键词
区块链/智能合约/漏洞检测/预训练模型/图神经网络Key words
blockchain/smart contract/vulnerability detection/pre-training model/graph neural network分类
信息技术与安全科学引用本文复制引用
庄园,樊泽楷,王诚,孙建国,李耀麟..基于预训练与新型时序图神经网络的智能合约漏洞检测方法[J].通信学报,2024,45(9):101-114,14.基金项目
国家自然科学基金资助项目(No.62202121) （No.62202121）
国家重点研发计划基金资助项目(No.2022YFB4400703) （No.2022YFB4400703）
中央高校基本科研业务费专项资金资助项目(No.3072022TS0604)The National Natural Science Foundation of China(No.62202121),The National Key Research and Develop-ment Program of China(No.2022YFB4400703),The Fundamental Research Funds for the Central Universities(No.3072022TS0604) （No.3072022TS0604）
