Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach
Abstract
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.
BibTeX
@inproceedings{Zhang-al:ASE22,
author = {Zhuo Zhang and
Yan Lei and
Meng Yan and
Yue Yu and
Jiachi Chen and
Shangwen Wang and
Xiaoguang Mao},
title = {Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach},
booktitle = {ASE},
pages = {83:1--83:13},
publisher = {{ACM}},
year = {2022},
}