Wang Zhibo, Guoming Liu, Xu Hongzhen, You Shengyu, Ma Han, Wang Hongling
College of Information Engineering, East China University of Technology, Nanchang, Jiangxi, China.
Department of Computer Science and Technology, East China University of Technology, Nanchang, Jiangxi, China.
PeerJ Comput Sci. 2024 Sep 26;10:e2320. doi: 10.7717/peerj-cs.2320. eCollection 2024.
Smart contracts play an essential role in the handling and management of digital assets, where vulnerabilities can lead to severe security issues and financial losses. Current detection techniques are largely limited to identifying single vulnerabilities and lack comprehensive identification capabilities for multiple vulnerabilities that may coexist in smart contracts. To address this challenge, we propose a novel multi-label vulnerability detection model that integrates extractive summarization methods with deep learning, referred to as Ext-ttg. The model begins by preprocessing the data using an extractive summarization approach, followed by the deployment of a custom-built deep learning model to detect vulnerabilities in smart contracts. Experimental results demonstrate that our method achieves commendable performance across various metrics, establishing the effectiveness of the proposed approach in the multi-vulnerability detection tasks within smart contracts.
智能合约在数字资产的处理和管理中起着至关重要的作用，其中漏洞可能导致严重的安全问题和财务损失。当前的检测技术在很大程度上仅限于识别单个漏洞，并且缺乏对智能合约中可能共存的多个漏洞的全面识别能力。为应对这一挑战，我们提出了一种新颖的多标签漏洞检测模型，该模型将抽取式摘要方法与深度学习相结合，称为Ext-ttg。该模型首先使用抽取式摘要方法对数据进行预处理，然后部署定制的深度学习模型来检测智能合约中的漏洞。实验结果表明，我们的方法在各项指标上均取得了值得称赞的性能，证明了所提方法在智能合约多漏洞检测任务中的有效性。
