Automated Smart Contract Vulnerability Detection using Fine-tuned Large Language Models-Reference-Cited by-同舟云学术

Automated Smart Contract Vulnerability Detection using Fine-tuned Large Language Models

Published:2023-12-15 Issue: Volume: Page:19-23
ISSN:
Container-title:Proceedings of the 2023 6th International Conference on Blockchain Technology and Applications
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Author:

Yang Zhiju¹^ORCID,Man Gaoyuan²^ORCID,Yue Songqing³^ORCID

Affiliation:

1. Seattle University, USA

2. Arizona State University, USA

3. University of Wisconsin - Platteville, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3651655.3651658

Reference43 articles.

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2. K. Qin, L. Zhou, B. Livshits, and A. Gervais, “Attacking the defi ecosystem with flash loans for fun and profit,” in International Conference on Financi al Cryptography and Data Security. Springer, 2021, pp. 3–32.

3. Y. Xue, M. Ma, Y. Lin, Y. Sui, J. Ye, and T. Peng, “Cross-contract static analysis for detecting practical reentrancy vulnerabilities in smart contracts,” in 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2020

4. P. Daian, S. Goldfeder, T. Kell, Y. Li, X. Zhao, I. Bentov, L. Breidenbach, and A. Juels, “Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,” in 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 2020.

5. Zhang Zhuo Brian Zhang Wen Xu and Zhiqiang Lin. "Demystifying Exploitable Bugs in Smart Contracts." ICSE 2023.