Abstract
We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of smart contracts, learn the model layer by layer to achieve feature extraction and vulnerability identification, introduce batch normalization, Dropout processing and use improved model classifiers to improve the vulnerability identification accuracy, model convergence speed and generalization capability of smart contracts. We had conducted numerous experiments, and the experimental results showed that BiGAS Detection Model has a strong vulnerability detection ability. The accuracy of vulnerability detection reached 93.24%, and the F1-score was 93.17%. We compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities.
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References
Zhang, K.F., Zhang, S.L., Jin, S.: The security research of blockchain smart contract. J. Inf. Secur. Res. 5(3), 192–206 (2019)
Zou, W.Q., Lo, D., Kochhar, P.S.: Smart contract development: challenges and opportunities. IEEE Trans. Softw. Eng. 47, 2084–2106 (2019). https://doi.org/10.1109/TSE.2019.2942301
Hu, T., Liu, X., Chen, T.: Transaction-based classification and detection approach for Ethereum smart contract. Inf. Process. Manag. 58(2), 102462 (2021). https://doi.org/10.1016/j.ipm.2020.102462
Amiet, N.: Blockchain vulnerabilities in practice. ACM Digit. Libr. 2(2), Article no. 8 (2021)
Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fus. 55, 59–67 (2020)
Huang, H., Wei, Z., Yao, L.: A novel approach to component assembly inspection based on mask R-CNN and support vector machines. Information 10, 282 (2019)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010). https://doi.org/10.3390/info10090282
Agarap, A.F.: An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. Comput. Sci. (2017)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Alalshekmubarak, A., Smith, L.S.: A novel approach combining recurrent neural network and support vector machines for time series classification. In: 2013 9th International Conference. Proceedings: Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, pp. 42–47 (2013)
Tang, Y.: Deep learning using linear support vector machines (2013)
Agarap, A.F.M.: A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. In: Proceedings: the 2018 10th International Conference on Machine Learning and Computing (ICMLC), 26–30 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
Luu, L., Chu, D.H., Olickel, H.: Making smart contracts smarter. In: The 2016 ACM SIGSAC Conference. Proceedings: Computer and Communications Security (CCS), New York City, NY, USA, pp. 254–269 (2016)
Tsankov, P., Dan, A., Drachsler-Cohen, D.: Securify: practical security analysis of smart contracts. In: The 2018 ACM SIGSAC Conference. Proceedings: Computer and Communications Security, Toronto, Canada, pp. 67–82 (2018)
Qian, P., Liu, Z., He, Q.: Towards automated reentrancy detection for smart contracts based on sequential models. IEEE Access 8, 19685–19695 (2020)
Zhuang, Y., Liu, Z., Qian, P.: Smart contract vulnerability detection using graph neural network. In: The Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI, pp. 3283–3290 (2020)
Liu, Z., Qian, P., Wang, X.: Combining graph neural networks with expert knowledge for smart contract vulnerability detection. IEEE Trans. Knowl. Data Eng. (2021)
Funding
This work is sponsored by the National Natural Science Foundation of China under grant number No. 62172353. 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.
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Li, Y. et al. (2023). An Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_33
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DOI: https://doi.org/10.1007/978-3-031-28124-2_33
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