一句话：提出双阶段深度学习方法，在检测智能合约重入漏洞的同时定位导致漏洞的可疑语句。
智能合约被广泛用于区块链上的金融与商业交易自动化，在降低信任成本的同时，也因大量部署而暴露出多种漏洞。随着深度学习的发展，近期许多工作将深度学习用于部署前的漏洞检测，在判断智能合约是否存在漏洞方面取得了较好效果，但在定位导致所检出漏洞的可疑语句上仍不理想。针对这一问题，本文提出一种基于深度学习的双阶段方法，用于重入漏洞的检测与定位：第一阶段完成合约级漏洞判定，第二阶段在此基础上进一步定位应承担责任的可疑语句，以提升定位的准确性。摘要未给出具体模型结构与实验评估数据。
Smart contracts have been widely and rapidly used to automate financial and business transactions together with blockchains, helping people make agreements while minimizing trusts. With millions of smart contracts deployed on blockchain, various bugs and vulnerabilities in smart contracts have emerged. Following the rapid development of deep learning, many recent studies have used deep learning for vulnerability detection to conduct security checks before deploying smart contracts. These approaches show effective results on detecting whether a smart contract is vulnerable or not whereas their results on locating suspicious statements responsible for the detected vulnerability are still unsatisfactory.
@inproceedings{paperbot7191,
title = {Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach},
author = {Zhuo Zhang and Yan Lei and Meng Yan and Yue Yu and Jiachi Chen and Shangwen Wang and Xiaoguang Mao},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3560428}
}