基于混合语义的切片级智能合约重入漏洞检测

doi:10.3778/j.issn.1002-8331.2307-0339

摘要/Abstract

摘要： 针对现有基于深度学习的漏洞检测方法主要集中在源代码的单一表现形式上，无法完全捕获源代码中包含的丰富语义信息和结构信息，以及大多数方法基于函数粒度，检测样本存在大量与漏洞无关的冗余代码导致检测精度下降的问题，聚焦于智能合约最严重的漏洞之一，即重入漏洞，提出一种基于混合语义的切片级智能合约重入漏洞检测方法SCHyVulDect。根据漏洞特征关键字对智能合约进行切片操作，获得合约切片；构建合约切片的代码图，通过图注意力网络（graph attention network，GAT）提取其深层语义信息。并使用双向长短期记忆网络（bidirectional long-short term memory，Bi-LSTM）和注意力机制，提取切片代码的上下文序列特征，将提取的图结构特征和序列特征进行融合，从而进行漏洞检测。实验结果表明，SCHyVulDect检测重入漏洞的精确率、召回率和F1值分别为96.36%、94.45%、91.70%，比现有的基于深度学习的智能合约漏洞检测方法的精确率提高13.03~18.00个百分点，具有较好的检测效果。

关键词: 区块链, 智能合约, 漏洞检测, 图注意力网络, 混合语义

Abstract: In response to the current deep learning based vulnerability detection methods mainly focusing on a single representation of the source code, which cannot fully capture the rich semantic and structural information contained in the source code, as well as the fact that most methods are based on function granularity and have a large number of redundant code unrelated to vulnerabilities in the detection samples, resulting in a decrease in detection accuracy, this paper focuses on one of the most serious vulnerabilities of smart contracts, namely reentrancy vulnerabilities, and proposes a slice level smart contract reentrancy vulnerability detection method SCHyVulDetect based on hybrid semantics. This method first performs slicing operations on smart contracts based on vulnerability feature keywords to obtain contract slicing. Then, a code graph of contract slicing is constructed, and its deep semantic information is extracted through the graph attention network (GAT). It also uses the bidirectional long short-term memory (BiLSTM) and attention mechanism to extract the context sequence features of the slicing code, and finally fuses the extracted graph structure features and sequence features for vulnerability detection. The experimental results show that the accuracy, recall, and F1 values of SCHyVulDetect for detecting reentrancy vulnerabilities are 96.36%, 94.45%, and 91.70%, respectively, which is 13.03 to 18.00 percentage points higher than the accuracy of existing deep learning based smart contract vulnerability detection methods and has a good detection effect.

Key words: blockchain, smart contract, vulnerability detection, graph attention network, hybrid semantics

江姝晨, 牛保宁, 高彦. 基于混合语义的切片级智能合约重入漏洞检测[J]. 计算机工程与应用, 2025, 61(1): 321-329.

JIANG Shuchen, NIU Baoning, GAO Yan. Slice Level Smart Contract Reentrancy Vulnerability Detection Based on Hybrid Semantics[J]. Computer Engineering and Applications, 2025, 61(1): 321-329.

参考文献

[1] GAI K, GUO J, ZHU L, et al. Blockchain meets cloud computing: a survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2009-2030.
[2] SUN J, HUANG S, ZHENG C, et al. Mutation testing for integer overflow in ethereum smart contracts[J]. Tsinghua Science and Technology, 2022, 27(1): 27-40.
[3] MEHAR M I, SHIER C L, GIAMBATTISTA A, et al. Understanding a revolutionary and flawed grand experiment in blockchain: the DAO attack[J]. Journal of Cases on Information Technology, 2019, 21(1): 19-32.
[4] QIAO C, QIU J, TAN Z, et al. Evaluation mechanism for decentralized collaborative pattern learning in heterogeneous vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 13123-13132.
[5] ZHANG L, TANG S, LV L. An finite iterative algorithm for sloving periodic Sylvester bimatrix equations[J]. Journal of the Franklin Institute, 2020, 357(15): 10757-10772.
[6] YI C, CAI J, ZHU K, et al. A queueing game based management framework for fog computing with strategic computing speed control[J]. IEEE Transactions on Mobile Computing, 2022, 21(5): 1537-1551.
[7] 张潆藜, 马佳利, 刘子昂, 等. 以太坊Solidity智能合约漏洞检测方法综述[J]. 计算机科学, 2022, 49(3): 52-61.
ZHANG Y L, MA J L, LIU Z A, et al. Overview of vulnerability detection methods for ethereum solidity smart contracts[J]. Computer Science, 2022, 49(3): 52-61.
[8] FENG H, FU X, SUN H, et al. Efficient vulnerability detection based on abstract syntax tree and deep learning[C]//Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops. Piscataway: IEEE, 2020: 722-727.
[9] LV L, WU Z, ZHANG J, et al. A VMD and LSTM based hybrid model of load forecasting for power grid security[J]. IEEE Transactions on Industrial Informatics, 2022, 18(9): 6474-6482.
[10] OKEGBILE S D, CAI J, NIYATO D, et al. Human digital twin for personalized healthcare: vision, architecture and future directions[J]. IEEE Network, 2023, 37(2): 262-269.
[11] TANN W J, HAN X J, GUPTA S S, et al. Towards safer smart contracts: a sequence learning approach to detecting security threats[J]. arXiv:1811.06632, 2018.
[12] QIAN P, LIU Z, HE Q, et al. Towards automated reentrancy detection for smart contracts based on sequential models[J]. IEEE Access, 2020, 8: 19685-19695.
[13] WANG W, SONG J, XU G, et al. ContractWard: automated vulnerability detection models for ethereum smart contracts[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1133-1144.
[14] ZHUANG Y, LIU Z, QIAN P, et al. Smart contract vulnerability detection using graph neural networks[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. New York: ACM, 2021: 3283-3290.
[15] LIU Z, QIAN P, WANG X, et al. Combining graph neural networks with expert knowledge for smart contract vulnerability detection[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1296-1310.
[16] WU H, ZHANG Z, WANG S, et al. Peculiar: smart contract vulnerability detection based on crucial data flow graph and pre-training techniques[C]//Proceedings of the 2021 IEEE 32nd International Symposium on Software Reliability Engineering. Piscataway: IEEE, 2021: 378-389.
[17] 赵波, 上官晨晗, 彭小燕, 等. 基于语义感知图神经网络的智能合约字节码漏洞检测方法[J]. 工程科学与技术, 2022, 54(2): 49-55.
ZHAO B, SHANGGUAN C H, PENG X Y, et al. Semantic-aware graph neural network for smart contract bytecode vulnerability detection[J]. Advanced Engineering Sciences, 2022, 54(2): 49-55.
[18] TIKHOMIROV S, VOSKRESENSKAYA E, IVANITSKIY I, et al. SmartCheck: static analysis of ethereum smart contracts[C]//Proceedings of the 2018 IEEE/ACM 1st International Workshop on Emerging Trends in Software Engineering for Blockchain. Piscataway: IEEE, 2018: 9-16.
[19] 刘泽润, 郑红, 邱俊杰. 基于抽象语法树裁剪的智能合约漏洞检测研究[J]. 计算机科学, 2023, 50(4): 317-322.
LIU Z R, ZHENG H, QIU J J. Smart contract vulnerability detection based on abstract syntax tree pruning[J]. Computer Science, 2023, 50(4): 317-322.
[20] ALLAMANIS M, BROCKSCHMIDT M, KHADEMI M. Learning to represent programs with graphs[J]. arXiv: 1711.00740, 2017.
[21] ROSSI R A, ZHOU R, AHMED N K. Deep inductive graph representation learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(3): 438-452.
[22] MITRA S, AVRA L J, MCCLUSKEY E J. Scan synthesis for one-hot signals[C]//Proceedings of the Proceedings International Test Conference 1997. Piscataway: IEEE, 1997: 714-722.
[23] WOLF L, HANANI Y, BAR K, et al. Joint word2vec networks for bilingual semantic representations[J]. Int J Comput Linguistics Appl, 2014, 5: 27-42.
[24] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[J]. arXiv:1710.10903, 2017.
[25] FERREIRA J F, CRUZ P, DURIEUX T, et al. Smartbugs: a framework to analyze solidity smart contracts[C]//Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. Piscataway: IEEE, 2020: 1349-1352.
[26] GHALEB A, PATTABIRAMAN K. How effective are smart contract analysis tools? evaluating smart contract static analysis tools using bug injection[C]//Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. New York: ACM, 2020: 415-427.
[27] MUELLER B. A framework for bug hunting on the ethereum blockchain[Z]. ConsenSys/mythril, 2017.
[28] TSANKOV P, DAN A, DRACHSLER-COHEN D, et al. Securify[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2018: 67-82.
[29] LUU L, CHU D H, et al. Making smart contracts smarter[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2016: 254-269.
[30] FEIST J, GRIECO G, GROCE A. Slither: a static analysis framework for smart contracts[C]//Proceedings of the 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain. Piscataway: IEEE, 2019: 8-15.