Abstract
Smart contracts have been under constant attack from outside, with frequent security problems causing great economic losses to the virtual currency market, and their security research has attracted much attention in the academic community. Traditional smart contract detection methods rely heavily on expert rules, resulting in low detection precision and efficiency. This paper explores the effectiveness of deep learning methods on smart contract detection and propose a multi-model smart contract detection method, which is based on a multi-model vulnerability detection method combining Bi-directional Gated Recurrent Unit (BiGRU) and Synthetic Minority Over-sampling Technique (SMOTE) for smart contract vulnerability detection. Through a comparative study on the vulnerability detection of 10312 smart contract codes, the method can achieve an identification accuracy of 90.17% and a recall rate of 97.7%. Compared with other deep network models, the method used in this paper has superior performance in terms of recall and accuracy.
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Acknowledgments
This study was supported by the National Key Research and Development Program of China (2020YFB1005704).
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Song, S., Yu, X., Ma, Y., Li, J., Yu, J. (2024). Multi-model Smart Contract Vulnerability Detection Based on BiGRU. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_1
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DOI: https://doi.org/10.1007/978-981-99-8132-8_1
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