Effective combining source code and opcode for accurate vulnerability detection of smart contracts in edge AI systems

Automating transactions using smart contracts extends the functionality of blockchains and secures the decentralization of blockchains in edge AI systems. Whereas, since plenty of smart contracts are deployed to support various decentralized edge applications, the security vulnerabilities of smart contracts will lead to huge irreversible losses. To deal with this problem, many deep learning-based methods have been developed for vulnerability detection. However, most existing methods use only contract source codes for feature extraction, resulting in low accuracy. In contrast, we propose a method based on deep learning model to integrate both the features of contract source codes and opcodes for vulnerability detection. Particularly, the contextual features are extracted based on opcodes while the expert pattern features are extracted from the source codes. Using the real-world dataset of Ethereum smart contracts targeting reentrancy vulnerability, experiment results demonstrate that our method outperforms the state-of-the-art methods and achieves 96.89% accuracy and 95.41% F1-Score.