ETHIAD:一种新的可解释模型,用于检测以太坊上的非法账户
Jiarong Lu1, Bin Liao2, Yi Liu3
1College of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, PR China.
PloS one
|December 11, 2025
概括
本研究介绍了ETHIAD,这是一个可解释的机器学习模型,用于检测以太坊区块链上的非法账户. ETHIAD显著提高了欺诈检测的准确性和可解释性,超过了当前最先进的方法.
科学领域:
- * 区块链技术的使用
- * 网络安全 网络安全
- * 机器学习 * 机器学习
背景情况:
- *以太坊是分散应用程序 (Dapps),初始硬币发行 (ICO) 和分散金融 (DeFi) 的主要平台.
- *该平台越来越多地成为非法活动的目标,包括欺诈,洗钱和非法筹款.
- *现有的欺诈检测模型与不平衡的数据集作斗争,缺乏解释性.
研究的目的:
- * 提出一种新的,可解释的模型来检测以太坊网络上的非法账户.
- * 解决阶级不平衡和在欺诈检测中的模型解释性方面的挑战.
主要方法:
- *使用ADASYN过量采样和拉索特征选择进行数据集的预处理,以实现有效的交易结构建模.
- *使用XGBoost算法训练以太坊非法账户检测 (ETHIAD) 模型.
- *使用SHAP框架对影响非法账户的关键因素进行多视角分析.
主要成果:
- * ETHIAD实现了高性能指标:准确率为99.70%,精度为99.51%,回忆率为99.02%,F1得分为99.26%,AUC为99.45%.
- * 该模型与现有最先进的 (SOTA) 模型相比,表现优越,性能比现有模型高0.05%-1.1%.
- * SHAP框架提供了强大的解释性,确定了导致非法账户检测的关键因素.
结论:
- * ETHIAD为识别以太坊区块链上的非法活动提供了一个强大的和可解释的解决方案.
- * 该模型的高准确性和可解释性提高了以太坊生态系统的安全性和可信度.
- *这些发现有助于在区块链平台的网络安全中推进机器学习应用.
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