相关实验视频
平衡-BiEGCN:一个双向的EvolveGCN与一个类平衡的学习网络,用于动态异常检测在比特币
Entropy (Basel, Switzerland)
|October 28, 2025
概括
本研究介绍了平衡BiEGCN用于比特币交易异常检测. 这种新型网络有效地捕捉了长距离的时间依赖性,并平衡不平衡的数据,提高了检测准确性.
科学领域:
- 计算机科学 计算机科学
- 金融技术 金融技术
- 网络安全 网络安全
背景情况:
- 比特币交易异常检测对于金融市场稳定至关重要.
- 当前的动态图形模型在捕捉长距离的时间依赖性和处理交易数据中的类不平衡方面面临着挑战.
- 不正常样本的稀缺性使得有效的异常检测复杂化.
研究的目的:
- 提出一种新的方法,平衡-BiEGCN,用于增强比特币交易异常检测.
- 解决现有方法中捕捉远程时间依赖和阶级不平衡的局限性.
- 提高动态交易网络中异常检测的准确性和稳定性.
主要方法:
- 开发了双向EvolveGCN (Bi-EvolveGCN) 来增强远程时间依赖性的捕获.
- 集成了一个样本类转换 (CSCT) 分类器来生成难以区分的异常样本,解决类不平衡.
- 利用相邻距离适应性损失和对称空间调整损失函数来引导样本生成和优化空间分布.
主要成果:
- 与现有的基线方法相比,平衡-BiEGCN模型在异常检测方面表现优越.
- 在圆形数据集上的实验结果验证了拟议方法的有效性.
- 双向时间特征融合和阶级平衡学习显著改善了检测能力.
结论:
- 均衡BiEGCN在比特币交易异常检测方面取得了重大进展.
- 该模型处理动态模式和阶级不平衡的能力使其成为金融市场稳定的宝贵工具.
- 未来的工作可以探索时间依赖模型和样本生成技术的进一步改进.
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