在以太坊上使用机器学习算法检测庞氏骗局
Ifeyinwa Jacinta Onu1, Abiodun Esther Omolara2, Moatsum Alawida3
1Department of Computer Science, University of Abuja, Gwagwalada, Nigeria. sylviaajah57@gmail.com.
Scientific reports
|October 27, 2023
概括
这项研究引入了一种新的机器学习方法来检测以太坊区块链上的庞氏骗局. 随机森林模型实现了高准确度,在识别欺诈性金融活动方面超过了以前的方法.
科学领域:
- 网络安全 网络安全
- 金融技术 (金融科技)
- 机器学习应用 机器学习应用
背景情况:
- 庞氏骗局对网络安全构成重大威胁,特别是在贫困率高的地区.
- 目前用于检测庞氏骗局的现有方法,通常依赖于交易数据,存在精度限制和数据缺陷.
- 欺诈性在线业务的快速增长需要先进的检测技术.
研究的目的:
- 提出和评估一种基于机器学习的新方法,用于检测以太坊网络上的庞氏骗局.
- 为了比较随机森林 (RF),神经网络 (NN) 和K-Nearest Neighbor (KNN) 算法的性能,用于识别庞氏骗局.
- 通过减少相关特征的数量来提高庞氏骗局检测的效率.
主要方法:
- 利用了来自Kaggle的超过20,000个以太坊交易网络数据集.
- 预处理的数据集用于训练和评估机器学习模型:随机森林 (RF),神经网络 (NN) 和K-最近邻居 (KNN).
- 使用特征选择,将数据集的复杂性从70个特征降低到10个特征,同时保持准确性.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,准确度为0.94,类得分为0.8833,总得分为0.96667.
- 对比分析表明,拟议的射频模型比以前的检测方法取得了更高的准确性.
- 该方法成功地识别了主要的欺诈特征,在不影响检测能力的情况下显著减少了维度.
结论:
- 开发的机器学习方法,特别是使用随机森林,在检测以太坊区块链上的庞氏骗局方面是有效的.
- 这种方法为识别复杂的庞氏骗局在其生命周期的早期提供了一个强大的解决方案.
- 这些发现为打击金融欺诈和加强在线安全提供了宝贵的见解.
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