相关实验视频

EnCTN:一个增强的人工智能支持的深度学习框架,用于增强区块链交易的安全性

P Bhuvaneshwari¹, A Krishnaveni², Y Harold Robinson³

¹School of Computer Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. bhuvaneshwari.p@manipal.edu.

Scientific reports

|November 27, 2025

概括

本研究介绍了一种支持深度学习的区块链框架,用于安全的数据管理. 这种新的方法提高了数据的耐用性和匿名性,提高了异常检测的准确性.

科学领域:

人工智能的人工智能
区块链技术区块链技术
数据安全数据安全

背景情况:

深度学习为基于人工智能 (AI) 的区块链框架提供先进的解决方案.
确保区块链交易中的数据可靠性,保密性和匿名性至关重要.
现有的方法需要改进,以获得可靠的数据耐用性和传播.

研究的目的:

为增强数据耐用性和交易分析提出混合区块链和深度学习模型.
开发一个安全的,支持深度学习的区块链交易模型,解决保密和匿名问题.
改进区块链系统中的时间异常检测.

主要方法:

使用一个增强的卷积时间网络 (EnCTN) 进行交易分析.
采用了一个移动窗口提取技术,用于时间序列数据.
包含扩展卷积来捕捉远程依赖关系.
在以太坊使用Python实现了框架.

主要成果:

拟议的技术在几个参数上表现出了比现有方法更好的性能.
在NSL-KDD数据集上实现了增强的异常分类准确性.
该框架有效地检测时间异常,提高了计算效率.

结论:

混合区块链和深度学习方法为现实世界的异常检测提供了有效的解决方案.
EnCTN模型显著提高了区块链系统中的数据耐用性和传播.
该框架提供了时间异常的准确发现,并提高了计算效率.

关键词:

异常检测检测异常检测自动编码器自动编码器区块链区块链区块链区块链深度学习是一种深度学习.安全的安全的安全的安全的安全.智能交易是一种智能交易.时间卷积网络的时间卷积网络.

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