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物联网系统中的AI安全性和网络风险
Petar Radanliev1,2, David De Roure3, Carsten Maple4
1Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Frontiers in big data
|October 25, 2024
概括
本研究探讨了低内存物联网 (IoT) 设备中的网络风险. 它引入了一个依赖模型,以更好地评估和管理新兴技术中的网络风险.
科学领域:
- 网络安全 网络安全
- 风险管理 风险管理
- 物联网 (IoT) 的互联网.
背景情况:
- 低内存连接设备,如无人机和机器人,是新兴技术的组成部分.
- 物联网 (IoT) 设备的扩散带来了独特的网络安全挑战.
- 当前的风险管理方法可能无法充分解决物联网环境的复杂性.
研究的目的:
- 批判性地分析与低内存物联网设备相关的网络风险.
- 评估现有的物联网风险管理策略的适当性.
- 提出物联网风险评估和管理的增强方法.
主要方法:
- 对当前的风险评估方法进行批判性反思.
- 为物联网数据策略和网络风险量身定制的依赖模型的开发.
- 分析新方法如何影响网络风险政策和数据战略.
主要成果:
- 在管理低内存物联网设备的网络风险方面发现了挑战.
- 介绍了一种用于网络风险估计和评估的新型依赖模型.
- 证明了该模型对网络风险保险和企业级风险评估的适用性.
结论:
- 对物联网网络风险的全面理解对于有效的缓解至关重要.
- 拟议的依赖性模型为估计和评估物联网风险提供了合适的方法.
- 需要进一步的研究来解决物联网风险评估和管理中的剩余问题.
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