中北大学学报（自然科学版）2023，Vol.44Issue(6)：624-631,8.DOI:10.3969/j.issn.1673-3193.2023.06.006
基于欠采样和源代码图表征的以太坊庞氏骗局检测
Ethereum Ponzi Scheme Detection Based on Undersampling and Source Code Graph Representation
龚晓元 1刘冬明 1高峰 1师自通1
作者信息
- 1. 中北大学计算机科学与技术学院,山西太原 030051
- 折叠
摘要
Abstract
Aiming at the problems of unbalanced data categories,single source of features and inability to fully express the semantic and grammatical relationships and program dependencies of smart contracts in the detection of ethereum Ponzi schemes,a detection method was proposed based on undersampling and source code graph representation.This method used the Levenshtein algorithm to calculate the distance among most types of smart contracts in the training set,and then used the K-Means algorithm to cluster most types of smart contracts,and selectively discarded most types of contracts to ensure the distance between fraudulent contracts and normal contracts in the training set.The category was relatively bal-anced,and the sensitivity of the classifier to abnormal contracts was improved;the composition algo-rithm was improved for the code features of Ponzi scheme contracts,and the semantic syntax informa-tion and program dependencies of smart contracts were deconstructed by removing redundant features and adding new core nodes,which made it easier for the neural network to capture and learn the behav-ioral characteristics and capital flow patterns of fraudulent contracts.The results of experiments on the XBlock dataset show that the method proposed in this paper has a recall rate of 98％while ensuring pre-cision,which is superior to existing methods.关键词
以太坊/智能合约/庞氏骗局/类别不平衡/源程序构图/图神经网络Key words
ethereum/smart contract/Ponzi scheme/class imbalance/source program graph composi-tion/graph neural network分类
信息技术与安全科学引用本文复制引用
龚晓元,刘冬明,高峰,师自通..基于欠采样和源代码图表征的以太坊庞氏骗局检测[J].中北大学学报（自然科学版）,2023,44(6):624-631,8.
