Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (4): 317-322.doi: 10.11896/jsjkx.220300063

• Information Security • Previous Articles     Next Articles

Smart Contract Vulnerability Detection Based on Abstract Syntax Tree Pruning

LIU Zerun, ZHENG Hong, QIU Junjie   

  1. School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
  • Received:2022-03-07 Revised:2022-08-23 Online:2023-04-15 Published:2023-04-06
  • About author:LIU Zerun,born in 1998,postgraduate,is a member of China Computer Federation.His main research interests include blockchain and deep learning.
    ZHENG Hong,born in 1973,Ph.D,associate professor,postgraduate supervisor,is a member of China Computer Federation.Her main research interests include blockchain and deep learning.
  • Supported by:
    National Natural Science Foundation of China(61472139) and Industry University Research Project: Research on Key Technologies of Blockchain(H300-41819).

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Abstract: With the development of blockchain technology,smart contracts have been widely used in various fields,and Ethereum has become the largest smart contract platform.At the same time,the frequent smart contract vulnerabilities have caused huge economic losses.The vulnerability detection of smart contract has become the focus of research,while the previous smart contract vulnerability detection tools can not make good use of the syntax information of the contract source code.Aiming at the re-entrancy vulnerability of smart contract,firstly,this paper proposes SCDefender,a vulnerability detection tool based on deep learning.Taking the abstract syntax tree form of the Solidity source code of smart contract as the research object,the tree-based convolutional neural networks is used for vulnerability detection.Secondly,an abstract syntax tree pruning algorithm is proposed to remove the nodes irrelevant to the vulnerability detection task and retain the key information in the abstract syntax tree.The accuracy,recall rate and F1 value of SCDefender vulnerability detection is 81.43%,92.12% and 86.45% respectively,which has a good vulnerability detection effect.Ablation experiments show that the abstract syntax tree pruning algorithm has an important contribution to the vulnerability detection task of SCDefender.

Key words: Blockchain, Smart contract, Vulnerability detection, Abstract syntax tree, Deep learning

CLC Number: 

  • TP309
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