Year: 2024
Type: conference-paper
Abstract: The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy.However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses.Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities.This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods.In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities.Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts.Comparative experiments were performed on two public datasets, and the results indicate that compared to stateof-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts.
Authors: Qing Yu Quan, Cheng Zeng, Yi Feng Wang, Jiao Ran Wang
Institution: Hubei University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G256562790, G3721195576, G8950497980
OpenAlex ID: W4406255696