Smart contract vulnerabilities detection with bidirectional encoder representations from transformers and control flow graph
Author:
Funder
National Key Research and Development Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01406-9.pdf
Reference43 articles.
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2. Ashraf, I., Ma, X., Jiang, B., et al.: Gasfuzzer: fuzzing ethereum smart contract binaries to expose gas-oriented exception security vulnerabilities. IEEE Access 8, 99552–99564 (2020). https://doi.org/10.1109/ACCESS.2020.2995183
3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014)
4. Cai, J., Li, B., Zhang, T., et al.: Fine-grained smart contract vulnerability detection by heterogeneous code feature learning and automated dataset construction. J. Syst. Softw. 209, 111919 (2024). https://doi.org/10.1016/j.jss.2023.111919
5. Chen, J., Xia, X., Lo, D., et al.: Defectchecker: automated smart contract defect detection by analyzing evm bytecode. IEEE Trans. Softw. Eng. 48, 2189–2207 (2021). https://doi.org/10.1109/TSE.2021.3054928
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