一个深度剩余的1D-CNN,在虚拟经济中对欺诈交易的检测有自我关注
Kamel K Mohammed1,2, Asmaa S Abdo3,4, Ashraf Darwish5,6,4
1Center for Virus Research and Studies, Al-Azhar University, Cairo, 11754, Egypt. vrsc@azhar.edu.eg.
Scientific reports
|February 11, 2026
概括
我们开发了一种新的1D-CNN模型,用于实时的元宇宙金融欺诈检测. 这种先进的系统准确地分类交易风险,增强虚拟经济中的安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 金融技术 金融技术
背景情况:
- 超宇宙中的虚拟经济正在迅速扩张,需要先进的金融交易安全性.
- 传统的欺诈检测方法与元宇宙数据的速度和复杂性作斗争.
研究的目的:
- 引入适用于元宇宙金融交易的实时异常检测和风险分类模型.
- 提高虚拟环境中欺诈检测的准确性和效率.
主要方法:
- 一个一维的卷积神经网络 (1D-CNN) 架构,增强了剩余连接和自我注意机制.
- 培训和验证元观金融数据集和信用卡欺诈检测数据集.
- 废除研究,以评估对数据噪声和缺陷的稳定性.
主要成果:
- 1D-CNN模型在将元宇宙交易分为低风险,中等风险和高风险级别时,实现了卓越的准确性,灵敏性和特异性.
- 该模型在专用元宇宙数据集和信用卡欺诈检测数据集上都表现出强的表现.
- 解释性分析证实了高维空间中风险水平的明显分离.
结论:
- 拟议的1D-CNN架构是有效和可靠的金融欺诈检测在元宇宙.
- 该模型处理数据不确定性的能力提高了其在现实世界中的适用性.
- 这种方法在确保虚拟经济交易方面取得了重大进展.
关键词:
卷积神经网络 (CNN) 是一种神经网络.金融交易是金融交易.欺诈检测 欺诈检测 欺诈检测超级变态 (Metaverse) 是一个变态.风险分类风险分类 风险分类自我注意力机制机制虚拟经济体 虚拟经济体更多相关视频
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