摘要
针对区块链上存在的欺诈账户给交易带来的安全问题,提出了基于机器学习的欺诈账户的检测及特征分析模型,将以太坊上真实的链上数据进行特征提取后作为模型的数据来源,通过对不同的机器学习方法进行比较得到最优模型并进行迭代训练以获得最佳的预测模型,同时引入SHAP值对数据特征进行分析。实验结果表明,基于XGBoost的欺诈账户检测模型在RMSE、MAE和R^(2)三组指标上达到了0.205、0.084和0.833,优于其余的对比模型,并结合SHAP值识别出预测欺诈账户的关键因素,为区块链的交易安全提供决策参考。
Aiming at the security problems caused by fraudulent accounts in the blockchain,this paper proposed a machine learning-based fraud account detection and feature analysis model.It took the real on-chain data on Ethereum into feature extraction and used it as the data source of the model.It obtained the optimal model by comparing different machine learning methods and performing iterative training to obtain the best predictive model.At the same time,it introduced SHAP value to analyze data characteristics.The experimental results show that the fraud account detection model based on XGBoost achieves 0.205,0.084 and 0.833 in RMSE,MAE and R^(2),which is better than the other comparison models.Combined with the SHAP value,it identifies the key factors that predict fraudulent accounts to provide decision-making reference for the transaction security of blockchain.
作者
周健
张杰
闫石
Zhou Jian;Zhang Jie;Yan Shi(School of Management Science&Engineering,Anhui University of Finance&Economics,Bengbu Anhui 233000,China;School of Computer Science,Beijing University of Posts&Telecommunications,Beijing 100876,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第4期992-997,共6页
Application Research of Computers
基金
国家自然科学基金资助项目(61402001)
安徽省高等学校自然基金资助项目(KJ2020A0013,KJ2019A0657,KJ2018A0441)
安徽财经大学研究生科研创新基金资助项目(ACYC2020349)。
关键词
链上数据
机器学习
区块链
欺诈账户
SHAP值
on-chain data
machine learning
blockchain
fraudulent account
SHAP value
