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A Novel Smart Contract Reentrancy Vulnerability Detection Model based on BiGAS

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Abstract

With the development of blockchain technology, smart contracts have attracted a lot of attention in recent years. They are widely used because they can reduce the cost of trust compared with traditional contracts. At the same time, they are at risk of being hacked. Therefore, the current research on smart contract vulnerability detection is particularly important. We proposed a novel smart contract reentrancy vulnerability detection model based on BiGAS. We had conducted numerous experiments, and the experimental results showed that our model (BiGAS Detection Model) has a strong vulnerability detection ability. It achieves an accuracy and F1-score of over 93% for the detection of reentrancy vulnerabilities in smart contracts. To verify that the choice of SVM is one of the reasons for improving the performance of our method, Softmax was replaced by the SVM classifier in the model. The accuracy of the model with the classifier replaced with Softmax was 89.78% and the F1-score was 89.83%. In addition, we compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. Compared with the existing advanced methods, the accuracy and F1-score improvement of our model ranges from 4 to 23%. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities.

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Data Availability

The experimental data and associated code used in this study have been deposited in the GitHub repository (https://github.com/wobulijie10086/deection/tree/master)

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Funding

This work is sponsored by the National Natural Science Foundation of China under grant number No. 62172353, U20B2046. And Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education under grant number No.1221045. Joint Research Fund of Guangzhou and University under Grant No. 202201020380. And Guangdong Higher Education Innovation Group 2020KCXTD007.

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Authors and Affiliations

  1. Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Lejun Zhang, Ran Guo, Jing Qiu, Shen Su, Yuan Liu, Guangxia Xu & Zhihong Tian

  2. Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing, 100039, China

    Lejun Zhang & Guopeng Wang

  3. College of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Lejun Zhang & Yuan Li

  4. Quanzhou Normal Univ, Sch Math & Comp Sci, Fujian, 362000, China

    Lejun Zhang

  5. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Huiling Chen

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  1. Lejun Zhang
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Correspondence to Lejun Zhang, Ran Guo or Guopeng Wang.

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Zhang, L., Li, Y., Guo, R. et al. A Novel Smart Contract Reentrancy Vulnerability Detection Model based on BiGAS. J Sign Process Syst 96, 215–237 (2024). https://doi.org/10.1007/s11265-023-01859-7

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  • DOI: https://doi.org/10.1007/s11265-023-01859-7

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