Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography.

Journal of visualized experiments : JoVE·2016
Same author

MiR-15a contributes abnormal immune response in myasthenia gravis by targeting CXCL10.

Clinical immunology (Orlando, Fla.)·2016
Same author

Minicells, Back in Fashion.

Journal of bacteriology·2016
Same author

A new variant of rabbit hemorrhagic disease virus G2-like strain isolated in China.

Virus research·2016
Same author

Tumour-suppressive role of PTPN13 in hepatocellular carcinoma and its clinical significance.

Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine·2016
Same author

Gonyautoxin 1/4 aptamers with high-affinity and high-specificity: From efficient selection to aptasensor application.

Biosensors & bioelectronics·2016

相关实验视频

Updated: Jun 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

比特币洗钱检测通过子图对比学习.

Shiyu Ouyang1, Qianlan Bai2, Hui Feng1

  • 1School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
概括

这项研究介绍了Bit-CHetG,这是一种新的图形学习方法,通过分析行为模式来检测加密货币洗钱群体. 它比现有方法显著提高了检测准确度.

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 数据科学数据科学数据科学

背景情况:

  • 加密货币的扩散加剧了洗钱活动.
  • 现有的欺诈检测通常侧重于个别交易,缺少群体行为.
  • 在比特币洗钱中,有组织,异质和杂的数据带来了检测挑战.

研究的目的:

  • 开发一种新的算法来检测比特币中的洗钱团体.
  • 解决当前欺诈检测方法中节点分类的局限性.
  • 为了发现洗钱实体之间的行为模式差异.

主要方法:

  • 提出了Bit-CHetG,这是一个基于分图的对比学习算法,用于异质图.
  • 利用预定义的元路来从比特币交易数据中构建同质子图.
  • 采用图形神经网络进行拓嵌入和监督对比学习以减轻噪音.

主要成果:

  • 通过捕捉异质性和行为模式,Bit-CHetG可以有效地检测洗钱团体.
  • 该算法在真实世界数据集上表现出更好的性能.
  • 与现有方法相比,微型F1得分至少增加了5%.

结论:

关键词:
比特币 比特币 比特币 比特币防止洗钱和反洗钱.相反的学习学习学习.图表神经网络的神经网络不同质的图形是不同的图形.

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K

相关实验视频

Last Updated: Jun 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
  • 基于子图的对比学习对于检测有组织的洗钱集团是有效的.
  • 比特-CHetG在加密货币欺诈检测方面取得了重大进展.
  • 该方法提高了识别复杂非法金融网络的能力.