基于数据流图和混合网络模型的智能合约漏洞检测
Vulnerability Detection of Smart Contracts Based on Data Flow Graph and Hybrid Network Model
丁诗琪 1陈正奎 1黄海1
作者信息
- 1. 浙江理工大学计算机科学与技术学院,浙江 杭州 310018
- 折叠
摘要
智能合约控制着区块链上巨额资产的流动,因此确保其安全性至关重要.基于此,提出一种基于数据流图和混合深度学习模型的方法,即DFG-HDP,用于检测智能合约的漏洞.该方法首先对智能合约源码进行清洗和变量规范;其次从源码中提取数据流特征,将其与源码结合作为输入;最后将不同的词嵌入模型与不同的深度学习模型结合,对输入进行学习检测.实验结果表明,该方法在智能合约漏洞检测中的F1值高达89.90%,优于之前的漏洞检测方法CBGRU.这一结果证明了该方法的有效性和优越性.
Abstract
Smart contracts control the flow of substantial assets on the blockchain,making their security crucial.Accordingly,this paper proposes a method called DFG-HDP(Data Flow Graph and Hybrid Deep Learning Model)for detecting vulnerabilities in smart contracts.This method first cleans and normalizes variables in the smart contract source code.Secondly,it extracts data flow features from the source code and combines them with the source code for input.Finally,it integrates various word embedding models with different deep learning models to learn and detect vulnerabilities from the input.Experimental results indicate that this method achieves an F1 score of 89.90%in smart contract vulnerability detection,outperforming the previous vulnerability detection method CBGRU.This result demonstrates the effectiveness and superiority of the proposed method.
关键词
智能合约/漏洞检测/数据流图/混合模型Key words
smart contracts/vulnerability detection/data flow graph/hybrid model引用本文复制引用
出版年
2025
NETL
NSTL
万方数据