基于局部图匹配的智能合约重入漏洞检测方法

doi:10.3969/j.issn.1671-1122.2022.08.001

信息网络安全 ›› 2022, Vol. 22 ›› Issue (8): 1-7.doi: 10.3969/j.issn.1671-1122.2022.08.001

基于局部图匹配的智能合约重入漏洞检测方法

张玉健¹^,²(), 刘代富¹, 童飞¹^,²

1.东南大学网络空间安全学院,南京 211189
2.江苏省泛在网络安全工程研究中心,南京 211189

收稿日期:2022-04-11 出版日期:2022-08-10 发布日期:2022-09-15
通讯作者: 张玉健 E-mail:yjzhang@seu.edu.cn
作者简介:张玉健（1984—）,男,江苏,讲师,博士,主要研究方向为区块链和系统安全。|刘代富（1996—）,男,四川,硕士研究生,主要研究方向为区块链和漏洞检测|童飞（1987—）,男,安徽,副教授,博士,主要研究方向为物联网及安全
基金资助:
国家自然科学基金(61971131);江苏省自然科学基金(BK20190346)

Reentrancy Vulnerability Detection in Smart Contracts Based on Local Graph Matching

ZHANG Yujian¹^,²(), LIU Daifu¹, TONG Fei¹^,²

1. School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
2. Jiangsu Province Engineering Research Center of Security for Ubiquitous Network, Nanjing 211189, China

Received:2022-04-11 Online:2022-08-10 Published:2022-09-15
Contact: ZHANG Yujian E-mail:yjzhang@seu.edu.cn

摘要/Abstract

摘要：

针对以太坊中智能合约遭受重入漏洞攻击的问题,文章提出一种基于局部图匹配的智能合约重入漏洞检测方法。该方法首先将智能合约源代码转化为包含基本结构信息的抽象语法树,并根据重入漏洞的特点裁剪抽象语法树;然后从抽象语法树中提取更加丰富的控制流和数据流,进而生成包含语法和语义信息的局部抽象语义图数据。文章利用图匹配神经网络对局部抽象语义图进行模型训练和测试,使用开源智能合约漏洞样本数据集生成测试数据并对方案进行评估。实验结果表明,该方法能够有效检测智能合约中的重入漏洞。

关键词: 智能合约, 图匹配神经网络, 重入漏洞, 抽象语义图

Abstract:

Aiming at the reentrancy vulnerability detection in smart contracts, this paper presents a code vulnerability detection method based on graph matching neural network, which realizes a function-level code vulnerability detection. Firstly, the smart contract source code is compiled into abstract syntax tree(AST), which is pruned according to the characteristics of reentrance vulnerabilities. Then, the control flow graph and data flow graph containing richer structural information are extracted from the abstract syntax tree, and then the local abstract semantic graph(ASG) data containing syntax and semantic information is generated. Furthermore, the graph matching neural network is used to train and test the local graph. Finally, an open-source vulnerability sample data set is used to evaluate the proposed method. Experimental results show that the proposed method effectively improves the ability of detecting reentrancy vulnerability.

Key words: smart contract, graph matching neural network, reentrancy vulnerability, abstract semantic graph

中图分类号:

TP309

张玉健, 刘代富, 童飞. 基于局部图匹配的智能合约重入漏洞检测方法[J]. 信息网络安全, 2022, 22(8): 1-7.

ZHANG Yujian, LIU Daifu, TONG Fei. Reentrancy Vulnerability Detection in Smart Contracts Based on Local Graph Matching[J]. Netinfo Security, 2022, 22(8): 1-7.

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参考文献 19

[1]	SCHOLLMEIER R. A Definition of Peer-to-Peer Networking for the Classification of Peer-to-Peer Architectures and Applications[C]//IEEE. 1st International Conference on Peer-to-Peer Computing. New York: IEEE, 2001: 101-102.
[2]	SlowMist. The Total Amount of Money Lost[EB/OL]. (2021-07-05) [2021-08-05]. https://hacked.slowmist.io/?c=ETH%20DApp.
[3]	BUTERIN V. ETHEREUM: A Next-Generation Cryptocurrency and Decentralized Application Platform[EB/OL]. (2015-07-14)[2021-09-18]. https://blockchainlab.com/pdf/Ethereum_white_paper-a_next_generation_smart_contract_and_decentralized_application_platform-vitalik-buterin.pdf.
[4]	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(JCIT), 2019, 21(1): 19-32.
[5]	WANG Bin, LIU Han, LIU Chao, et al. Blockeye: Hunting for Defi Attacks on Blockchain[C]//IEEE. 2021 IEEE/ACM 43rd International Conference on Software Engineering:Companion Proceedings(ICSE-Companion). New York: IEEE, 2021: 17-20.
[6]	JIANG Bo, LIU Ye, CHAN W K. ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection[C]//ACM. 33rd ACM/IEEE International Conference on Automated Software Engineering. New York: ACM, 2018: 259-269.
[7]	ZHANG Qingzhao, WANG Yizhuo, LI Juanru, et al. Ethploit: From Fuzzing to Efficient Exploit Generation against Smart Contracts[C]//IEEE. 27th International Conference on Software Analysis, Evolution and Reengineering(SANER). New York: IEEE, 2020: 116-126.
[8]	LUU L, CHU D H, OLICKEL H, et al. Making Smart Contracts Smarter[C]//ACM. 2016 ACM SIGSAC Conference on Computer and Communications Security(CCS). New York: ACM, 2016: 254-269.
[9]	PRECHTEL D, GROS T, MÜLLER T. Evaluating Spread of ‘Gasless Send’ in Ethereum Smart Contracts[C]//IEEE. 2019 10th IFIP International Conference on New Technologies, Mobility and Security(NTMS). New York: IEEE, 2019: 1-6.
[10]	BHARGAVAN K, DELIGNAT-LAVAUD A, FOURNET C, et al. Formal Verification of Smart Contracts: Short Paper[C]//ACM. 2016 ACM Workshop on Programming Languages and Analysis for Security(PLAS). New York: ACM, 2016: 91-96.
[11]	TSANKOV P, DAN A, DRACHSLER-COHEN D, et al. Securify: Practical Security Analysis of Smart Contracts[C]//ACM. 2018 ACM SIGSAC Conference on Computer and Communications Security(CCS). New York: ACM, 2018: 67-82.
[12]	TIKHOMIROV S, VOSKRESENSKAYA E, IVANITSKIY I, et al. SmartCheck: Static Analysis of Ethereum Smart Contracts[C]//ACM. 1st International Workshop on Emerging Trends in Software Engineering for Blockchain. New York: ACM, 2018: 9-16.
[13]	FEIST J, GRIECO G, GROCE A. Slither: A Static Analysis Framework for Smart Contracts[C]//IEEE. 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain(WETSEB). New York: IEEE, 2019: 8-15.
[14]	GORI M, MONFARDINI G, SCARSELLI F. A New Model for Learning in Graph Domains[C]//IEEE. 2005 IEEE International Joint Conference on Neural Networks. New York: IEEE, 2005: 729-734.
[15]	CHANG R Y, PODGURSKI A, YANG J. Discovering Neglected Conditions in Software by Mining Dependence Graphs[J]. IEEE Transactions on Software Engineering, 2008, 34(5): 579-596. doi: 10.1109/TSE.2008.24 URL
[16]	LI Yujia, GU Chenjie, DULLIEN T, et al. Graph Matching Networks for Learning the Similarity of Graph Structured Objects[EB/OL]. (2019-07-12)[2021-09-18]. http://proceedings.mlr.press/v97/li19d.html.
[17]	ZHUANG Yuan, LIU Zhenguang, QIAN Peng, et al. Smart Contract Vulnerability Detection Using Graph Neural Network[EB/OL]. (2020-05-08)[2021-09-18]. https://www.ijcai.org/Proceedings/2020/0454. pdf.
[18]	GILMER J, SCHOENHOLZ S S, RILEY P F, et al. Neural Message Passing for Quantum Chemistry[EB/OL]. (2017-09-13)[2021-09-18]. https://proceedings.mlr.press/v70/gilmer17a.html.
[19]	FERREIRA J F, CRUZ P, DURIEUX T, et al. SmartBugs: A Frameworkto Analyze Solidity Smart Contracts[C]//IEEE. 35th IEEE/ACM International Conference on Automated Software Engineering. New York: IEEE, 2020: 1349-1352.

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实际样本分类	测试结果
实际样本分类	含重入漏洞	不含重入漏洞
漏洞样本	TP	FN
不含漏洞样本	FP	TN

	ACC	TPR	FPR	PRE	F1值
Securify	56.31%	43.60%	19.40%	81.69%	56.86%
Smartcheck	52.10%	42.85%	24.69%	51.83%	53.68%
Mythril	65.32%	50.68%	26.77%	62.11%	51.38%
Oyente	75.50%	70.58%	23.48%	65.70%	59.01%
DR-GCN	70.26%	74.51%	18.65%	77.72%	73.39%
本文方法	83.28%	80.21%	13.07%	87.95%	83.90%

基于局部图匹配的智能合约重入漏洞检测方法

Reentrancy Vulnerability Detection in Smart Contracts Based on Local Graph Matching

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