A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2024, 00, pp. 31-36
- Issue Date:
- 2024-01-03
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1693207 AM.pdf | Accepted version | 879.63 kB |
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Due to the rapid development of blockchain technology in recent years smart contracts have been widely applied in critical fields such as finance insurance healthcare and the Internet of Things However smart contracts face increasingly serious security issues due to their unique operating environment and programming characteristics We focus on Ethereum based smart contracts and propose a high precision and versatile detection method to address the integer overflow vulnerability which significantly affects smart contract development and execution Our method can also solve the problem of possible data shortage Specifically we utilize code embedding algorithms to convert Solidity compiled smart contracts into spatial vectors thereby retaining as much syntax and semantic information as possible Based on this we use Generative Adversarial Network GAN to train a small sample vector dataset to generate a substantial number of synthetic datasets Our proposed model combines GAN discriminator feedback and vector similarity analysis to identify smart contracts that contain integer overflow vulnerabilities
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