A smart contract vulnerability detection method based on deep learning with opcode sequences

Abstract

Ethereum is a blockchain network that allows developers to create smart contracts and programs that run on the blockchain. Smart contracts contain logic to transfer assets based on pre-defined conditions. With over 100,000 new smart contracts being deployed every day, the potential for coding errors is high, making the contracts vulnerable to exploits. A key limitation is that once deployed, smart contracts are immutable and cannot be updated, even if flaws are found. This inflexibility puts funds at risk of theft and loss. The rapid pace of deployment outpaces security audits, increasing vulnerabilities that put users’ cryptocurrency at risk. To reduce the risk caused by smart contract vulnerabilities, we applied deep learning techniques. To develop a deep learning model capable of detecting vulnerabilities, we first created a dataset by replaying real transactions on the Ethereum Mainnet, collecting opcode sequences from real Ethereum contracts, and labeling them using the SODA plugin. We pre-processed this opcode data by removing duplicates, normalizing sequence lengths, simplifying opcodes into representative groups, and converting sequences into numerical vectors to ultimately obtain an optimal representation of the data. We then trained and evaluated three different neural network architectures on this dataset. Our best-performing model achieved an average accuracy of 88% in detecting seven types of vulnerabilities. Further analysis showed that the model was effective at identifying potential problems in smart contracts, which was an important capability for securing funds and executing logic in live contracts.