基于GR算法的区块链智能合约漏洞检测

王德广; 单梦桃; 李凤岐; 佟宁

doi:10.11959/j.issn.2096-109x.2025018

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基于GR算法的区块链智能合约漏洞检测

学术论文 | 更新时间：2025-05-14

- 基于GR算法的区块链智能合约漏洞检测
- Blockchain smart contract vulnerability detection based on GR algorithm
- 网络与信息安全学报 2025年11卷第2期页码：76-86
- 作者机构：
  
  大连交通大学软件学院，辽宁大连 116028
- 作者简介：
  
  [ "王德广（1968- ），男，辽宁大连人，大连交通大学副教授，主要研究方向为信息安全、机器学习。" ]
  [ "单梦桃（1997- ），女，河南驻马店人，大连交通大学硕士生，主要研究方向为区块链、智能合约漏洞、机器学习。" ]
  [ "李凤岐（1974- ），男，河北承德人，大连交通大学教授、博士生导师，主要研究方向为区块链技术、工业互联网、大数据与智能计算。" ]
  [ "佟宁（1981- ），女，辽宁盘锦人，大连交通大学副教授，主要研究方向为无线网络安全、人工智能、区块链。" ]
- 基金信息：
  
  辽宁省国际科技合作项目(2022JH2/10700012);辽宁省应用基础研究项目(2023JH2/101300188;2022JH2/101300269);云南省服务计算重点实验室基金(YNSC23118);辽宁省教育厅基础研究项目(JYTMS20230011)
- DOI：10.11959/j.issn.2096-109x.2025018
  中图分类号： TP393
- 收稿：2024-09-27，
  
  修回：2025-02-10，
  
  纸质出版：2025-04-25
- 稿件说明：
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王德广, 单梦桃, 李凤岐, 等. 基于GR算法的区块链智能合约漏洞检测[J]. 网络与信息安全学报, 2025, 11(2): 76-86.

WANG D G, SHAN M T, LI F Q, et al. Blockchain smart contract vulnerability detection based on GR algorithm[J]. Chinese Journal of Network and Information Security, 2025, 11(2): 76-86.
王德广, 单梦桃, 李凤岐, 等. 基于GR算法的区块链智能合约漏洞检测[J]. 网络与信息安全学报, 2025, 11(2): 76-86. DOI： 10.11959/j.issn.2096-109x.2025018.

WANG D G, SHAN M T, LI F Q, et al. Blockchain smart contract vulnerability detection based on GR algorithm[J]. Chinese Journal of Network and Information Security, 2025, 11(2): 76-86. DOI： 10.11959/j.issn.2096-109x.2025018.

摘要

智能合约作为区块链技术的核心执行载体，其安全性是确保链上数字资产有效管理的基石。针对当前智能合约漏洞检测存在覆盖漏洞类型有限、检测效率偏低的技术瓶颈，提出重点解决多类型漏洞检测与特征提取效率优化两大核心问题。为此，构建了包含3 000余条样本的5个专项数据集，覆盖可重入漏洞、时间戳依赖漏洞、整数型溢出漏洞、交易顺序依赖和交易授权漏洞等主流漏洞类型，为多种类检测奠定数据基础；在算法设计层面，提出改进的GR（gated recurrent unit- random forest）算法模型，其中改进型门控循环神经网络通过引入分离注意力机制强化关键漏洞特征的捕捉能力，而随机森林算法则采用信息熵优化策略实现全局特征的完整性保留，双通道处理架构既保证了关键漏洞特征的显著性提取，又避免了深层特征在传递过程中的信息衰减，旨在提升智能合约漏洞检测的种类数与效率。实验结果显示：GR算法模型能够识别出5种智能合约漏洞，准确率高达98.88％。相较于先前算法模型，检测效率提高3%以上，漏洞类型平均增加3种，验证了GR算法模型的可行性和优越性。

Abstract

Smart contract

as the core execution mechanism in blockchain technology

have their security serving as the cornerstone for the effective management of on-chain digital assets. To address the current technical bottlenecks in smart contract vulnerability detection

such as limited coverage of vulnerability categories and detection efficiency

the study was conducted. Two critical challenges were focused on: multi-category vulnerability detection and optimization of feature extraction efficiency. Methodologically

the following approaches were adopted: Five specialized datasets containing more than 3

000 annotated samples were constructed

covering mainstream vulnerability categories including reentrant vulnerabilities

timestamp dependency vulnerabilities

integer overflow vulnerabilities

transaction order dependence and transaction authorization vulnerabilities. Thus

a robust data foundation for multi-category detection was established. At the algorithmic design level

an improved GR (gated recurrent unit-random forest) algorithm model was proposed. In this model

the improved gated recurrent neural network (GRU) incorporated a decoupled attention mechanism to strengthen the ability to capture critical vulnerability features. Meanwhile

the random forest algorithm employed an information entropy optimization strategy to preserve the integrity of global features. The dual-channel processing architecture not only ensured the significant extraction of key vulnerability features

but also prevented information attenuation during deep feature transmission. Ultimately

the aim of improving both the variety and efficiency of smart contract vulnerability detection was achieved. Experimental results showed that the GR algorithm model could successfully detect five categories of smart contract vulnerabilities with an accuracy rate of 98.88%. Compared to previous algorithm models

it achieved over 3% improvement in detection efficiency and increased three detectable vulnerability categories. Thus

the feasibility and superiority of the GR algorithm model were validated.

关键词

Keywords

references

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