网络与信息安全学报2025，Vol.11Issue(2)：76-86,11.DOI:10.11959/j.issn.2096-109x.2025018
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
摘要
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 effi-ciency,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 vulner-ability categories including reentrant vulnerabilities,timestamp dependency vulnerabilities,integer overflow vulner-abilities,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 in-tegrity 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.Experi-mental results showed that the GR algorithm model could successfully detect five categories of smart contract vul-nerabilities with an accuracy rate of 98.88%.Compared to previous algorithm models,it achieved over 3%im-provement in detection efficiency and increased three detectable vulnerability categories.Thus,the feasibility and superiority of the GR algorithm model were validated.关键词
区块链/智能合约/数据集预处理/漏洞检测算法Key words
blockchain/smart contract/dataset preprocessing/vulnerability detection algorithm分类
计算机与自动化引用本文复制引用
王德广,单梦桃,李凤岐,佟宁..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):76-86,11.基金项目
辽宁省国际科技合作项目(2022JH2/10700012) （2022JH2/10700012）
辽宁省应用基础研究项目(2023JH2/101300188,2022JH2/101300269) （2023JH2/101300188,2022JH2/101300269）
云南省服务计算重点实验室基金(YNSC23118) （YNSC23118）
辽宁省教育厅基础研究项目(JYTMS20230011) Liaoning Province International Science and Technology Cooperation Project(2022JH2/10700012),Liaon-ing Province Applied Basic Research Project(2023JH2/101300188,2022JH2/101300269),Yunnan Provincial Key Laboratory Fund for Service Computing(YNSC23118),Basic Research Project of Liaoning Provincial Department of Education(JYTMS20230011) （JYTMS20230011）
