重庆大学学报2024，Vol.47Issue(12)：70-82,13.DOI:10.11835/j.issn.1000.582X.2024.12.007
基于混合风格迁移的智能合约漏洞检测方法
Smart contract vulnerability detection method based on MixStyle transfer
摘要
Abstract
This study presents a smart contract vulnerability detection method using MixStyle transfer to address challenges related to limited datasets and the detection of unknown vulnerabilities when new ones arise in smart contracts.The method first extracts the abstract syntax tree from the smart contract source code and uses a graph attention network to capture dependencies and information flow between nodes.Then,maximum mean discrepancy(MMD)is used to facilitate effective knowledge transfer from known vulnerabilities to emerging ones,thus expanding the dataset available for deep learning model training.Finally,the MixStyle technique is incorporated into the classifier to enhance model generalization and improve the accuracy of identifying novel vulnerability types.Experimental results show that this method outperforms BLSTM-ATT,BiGAS,and Peculiar methods in F1,ACC,and MCC metrics for detecting four types of vulnerabilities.关键词
智能合约/漏洞检测/迁移学习/MixStyle/最大均值差异Key words
smart contracts/vulnerability detection/transfer learning/MixStyle/maximum mean discrepancy分类
信息技术与安全科学引用本文复制引用
李敏,时瑞浩,张莹,袁海兵,姜立标,缪丹云..基于混合风格迁移的智能合约漏洞检测方法[J].重庆大学学报,2024,47(12):70-82,13.基金项目
国家自然科学基金(61602345). Supported by National Natural Science Foundation of China(61602345). （61602345）
