以人工智能为驱动的方法来保护备份数据,并提高可持续智能基础设施的网络弹性
Basant Kumar1,2, Shashi Kant Gupta1,3, Rashmi Dwivedi4,5
1Lincoln University College, 47301, Petaling Jaya, Selangor, Malaysia.
Scientific reports
|March 17, 2026
概括
本研究介绍了一种使用Murmur Polytopes Hash (MPH) 来验证备份数据完整性的AI驱动方法,增强智能城市的网络弹性 (CR). 该方法确保恢复之前的数据完整性,改善智能基础设施对网络威胁的安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 智能城市由于连接性增加而面临重大网络威胁.
- 传统的网络弹性框架往往忽视了备份数据完整性验证.
- 这种差距损害了数据恢复和整体系统的弹性.
研究的目的:
- 实施人工智能驱动的方法来验证备份数据的完整性.
- 在智能基础设施中增强网络弹性 (CR).
- 解决传统弹性框架在数据恢复方面的局限性.
主要方法:
- 使用Murmur多层哈希 (MPH) 来验证备份数据的完整性.
- 开发了一个基于AI的勒索软件攻击检测模块,用于智能基础设施.
- 集成节点初始化,集群,数据安全和存储 (云和IPFS).
- 用Merkle树来生成和更新哈希代码.
主要成果:
- 实现了高安全级别和98.45% (安全性) 和98.65% (准确性) 的准确率.
- 通过有效的备份数据完整性检查,证明了更好的弹性.
- 在恢复之前使用MPH成功验证了备份数据完整性.
结论:
- 拟议的AI驱动的MPH方法显著提高了智能城市的网络弹性.
- 确保备份数据的完整性对于有效的数据恢复和安全至关重要.
- 该系统为保护智能基础设施免受网络威胁提供了强大的解决方案.
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