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相关实验视频

Updated: Jun 3, 2025

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Published on: May 22, 2016

在基于图形神经网络的区块链网络中检测异常节点.

Ze Chang¹, Yunfei Cai¹, Xiao Fan Liu²

¹School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

Sensors (Basel, Switzerland)

|January 11, 2025

概括

由于数据不平衡,区块链欺诈检测具有挑战性. 我们的新型图形注意力网络 (GAT) 方法SGAT-BC通过将子树注意力与集体学习 (Bagging和CAT) 结合起来,有效地识别欺诈性节点.

关键词:

检测异常检测异常检测区块链区块链区块链区块链区块链组合学习组合学习图表神经网络的神经网络

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科学领域:

计算机科学计算机科学
网络安全网络安全
数据挖掘数据挖掘

背景情况:

区块链技术的增长导致欺诈活动增加,威胁到用户资产.
区块链交易网络可以作为图形建模,欺诈性节点是异常的.
图形数据中的类不平衡,其中异常节点是少数的,挑战了传统的检测方法.

研究的目的:

为了解决区块链交易网络中检测异常节点的类失衡问题.
提出一种新的图形神经网络方法,改进现有的图形数据挖掘技术,用于欺诈检测.

主要方法:

开发了一种新的图形神经网络方法,SGAT-BC,增强图形注意网络 (GAT).
在GAT框架内纳入了一个子树注意力机制.
集成组合学习技术:引导集成 (袋装) 和分类提升 (CAT).

主要成果:

拟议的SGAT-BC方法与现有的基线模型相比,表现优越.
在四个现实世界的区块链交易数据集上进行了实验,验证了该方法的有效性.
在欺诈检测方面,SGAT-BC成功地克服了阶级不平衡所带来的挑战.

结论:

该SGAT-BC方法提供了一个强大的解决方案,用于检测区块链网络中的欺诈活动.
将GAT与子树注意力和集体学习相结合,对于不平衡的图形数据是一个有前途的方法.
这项研究有助于提高区块链交易的安全性和保护用户资产.