在比特币交易网络中非法社区检测
Dany Kamuhanda1,2,3, Mengtian Cui4, Claudio J Tessone1,2
1UZH Blockchain Center, University of Zurich, 8050 Zurich, Switzerland.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
在加密货币网络中,社区检测可以识别非法活动. 这项研究发现,0.06%的比特币社区包含非法地址,突出了社区质量优化和标签传播方法的有效性.
科学领域:
- 网络安全 网络安全
- 网络分析 网络分析
- 区块链技术 区块链技术
背景情况:
- 社区检测对于社会网络分析至关重要.
- 加密货币交易网络由于伪名和多地址所有权,对社区检测提出了独特的挑战.
- 识别这些网络中的非法活动是一个重大问题.
研究的目的:
- 调查社区检测方法在比特币交易网络中识别非法活动的有效性.
- 解决伪名地址和多地址所有权所带来的挑战.
- 确定最适合这个域的社区检测算法.
主要方法:
- 对比特币交易网络结构的分析.
- 收集和利用已知非法比特币地址的数据集,用于社区标签.
- 评估各种社区检测算法,包括基于距离的社区质量优化和标签传播方法.
主要成果:
- 根据2,313,344个非法地址的数据集,0.06%的检测到的社区包含一个或多个非法比特币地址.
- 基于远程的集群和网络表示学习方法被证明不适合比特币交易网络.
- 基于社区质量优化和标签传播的方法显示出最高的适用性和有效性.
结论:
- 社区检测是识别加密货币网络中的非法集群的可行方法.
- 建议使用标签传播和社区质量优化方法来分析比特币交易网络.
- 进一步的研究可以完善这些方法,以便在区块链生态系统中更好地检测金融犯罪.
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