Smart-Contract Vulnerability Detection Method Based on Deep Learning

With the rapid development of blockchain technology, smart contracts (SCs) applied in digital currency transactions have been widely used. However, SCs often have vulnerability in their code that allow criminals to exploit them to steal associated digital assets. Benefiting from the development of machine learning technology and the improvement of hardware performance, one can use deep learning techniques to analyze code and detect vulnerabilities. This paper proposes an innovative combination of opcode sequences and abstract syntax trees for source code parsing. And a method based on the combination of self-attention mechanism and bidirectional long-short term memory neural network is proposed to detect the vulnerability of SCs after word embedding. Experimentation results show that the two parsing methods can complement each other and effectively improve the accuracy of vulnerability detection.