A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning

【Author】 Zhang, Lejun; Wang, Jinlong; Wang, Weizheng; Jin, Zilong; Zhao, Chunhui; Cai, Zhennao; Chen, Huiling

【Source】SENSORS

【影响因子】3.847

【Abstract】Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.

【Keywords】smart contract; vulnerability detection; blockchain security; operation flow; Ensemble Learning; information graph

【发表时间】2022 MAY

【收录时间】2022-05-22

【文献类型】实证性文章

【主题类别】

区块链治理-技术治理-漏洞检测