SmartDT: An Effective Vulnerability Detection System of Smart Contracts Based on Deep Learning.

In recent years, blockchain technology has received widespread attention. Smart contracts are programs that run on the blockchain, and their security faces serious challenges for blockchain applications. Inspired by the success of artificial intelligence technology, some smart contract vulnerability detection methods based on deep learning have been proposed and achieved meaningful progress. However, a closer look reveals three flaws in these works. First, some works simply use LSTM modules to perform sequence learning on pre-processing smart contracts, which lacks the ability to extract long-range features and does not support parallel processing of inputs. Secondly, smart contract vulnerability detection methods based on deep learning lack interpretability of results. From the perspective of system design, in order to solve these problems, we propose SmartDT, a more effective, faster, and interpretable smart contract vulnerability detection system. Specifically, (i) we propose an attention-based deep learning smart contract detection module, which is able to learn long-range dependencies in inputs and supports parallel processing of inputs. (ii) After the deep learning module, we stack an optional symbolic execution module for enhancing the interpretability of the classification results. Compared with general symbolic execution detection, our method can achieve faster detection because we can call a specific symbolic analysis model to detect input based on the classification results of the deep learning module. Extensive experiments demonstrate the effectiveness of our proposed method. Compared with other machine learningbased methods, our method achieves better performance and better interpretability, which demonstrates SmartDT’s outstanding feature learning capabilities. Compared with symbolic execution methods, our method achieves faster and more effective detection. In addition, we conducted ablation experiments to verify the effectiveness of each module.