A BiLSTM-Attention Model for Detecting Smart Contract Defects More Accurately

【Author】 Qian, Chen; Hu, Tianyuan; Li, Bixin

【Source】2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY, QRS

【影响因子】

【Abstract】Smart contracts are applications running on the blockchain which control many virtual currencies. Since smart contracts are composed of code, they inevitably have defects. In recent years, many smart contract defects have caused lots of economic losses and harmful impacts. A contract that has defects may have some errors that cause unwanted results. As smart contracts cannot be modified once deployed, it is necessary to ensure that they are free from defects. In this paper, we focus on eleven defects of smart contracts and construct a deep learningbased model to detect these contract defects more accurately. Our model regards the smart contract's operation codes as a sequential sentence and uses an Attention-based bidirectional long short term memory (BiLSTM-Attention) model to find smart contract defects. We evaluate our model's and other models' performance on 45622 real-world smart contracts. The experimental results show that our model can achieve higher accuracy (95.40%) and F1-score (95.38%). In addition, our model is highly efficient and can quickly detect large numbers of contracts.

【Keywords】smart contract; deep learning; defect detection; blockchain

【发表时间】2022

【收录时间】2023-06-03

【文献类型】理论模型

【主题类别】

区块链治理-技术治理-智能合约漏洞检测