全球-本地图表关注比特币反洗钱检测的周期性伪标签
Meng Li1,2, Lu Jia3, XinQiao Su3
1College of Mathematics and Statistic, Hebei University of Economics and Business, Shijiazhuang, 050062, China. mli269-c@my.cityu.edu.hk.
Scientific reports
|July 2, 2025
概括
这项研究引入了变压器增强的图表注意网络 (TFGAT),以改善比特币交易中的洗钱检测. 我们的方法增强了信息提取,并有效地利用有限的数据,优于现有的反洗钱模型.
科学领域:
- 网络安全 网络安全
- 图形神经网络的神经网络
- 区块链分析 区块链分析
背景情况:
- 由于隐藏的非法交易,比特币洗钱存在复杂的挑战.
- 现有的图形卷积网络 (GCN) 方法在动态节点权重和全球信息提取方面遇到了困难.
- 有限的标记数据和数据注释的成本阻碍了反洗钱 (AML) 模型的有效性.
研究的目的:
- 开发一种先进的模型来检测比特币网络中的洗钱行为.
- 解决当前基于GCN的反洗钱方法在处理复杂交易模式方面的局限性.
- 为了有效地利用有限的标记数据进行强大的模型培训.
主要方法:
- 提出了一个改进变压器的图形注意力网络 (TFGAT),结合了全球-本地注意力机制 (GLATM).
- 利用变压器进行全球信息提取,并专注于本地节点信息.
- 引入了深度循环伪标签更新机制 (DCPLU),以增强数据分布和模型稳定性,而不依赖于多重或欧几里德假设.
主要成果:
- 与现有方法相比,TFGAT模型在各种指标上表现出优异的表现.
- 该GLATM有效地从比特币交易图表中提取全球和本地信息.
- 在有限的标记数据下,DCPLU机制提高了模型的稳定性和性能.
结论:
- 拟议的TFGAT模型与GLATM和DCPLU在检测比特币洗钱方面取得了重大进展.
- 该方法有效地解决了与动态权重,全球信息提取和数据稀缺性相关的挑战.
- 这种方法为区块链网络中的反洗钱提供了更强大,更有效的解决方案.
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