使用对抗式学习技术,对网络物理系统大量数据入侵的诊断行为分析
Shitharth Selvarajan1,2,3, Hariprasath Manoharan4, Maha Abdelhaq5
1Department of Computer Science, Kebri Dehar University, Kebri Dehar, Dehar , Ethiopia. ShitharthS@kdu.edu.et.
Scientific reports
|March 2, 2025
概括
本研究介绍了一个网络物理系统 (CPS) 框架,使用学习技术来检测和减轻数据入侵. 这种新的方法显著降低了数据丢失和故障率,达到97%的效率.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 网络物理系统 (CPS) 越来越容易受到数据入侵的影响,因为网络拓不断扩展.
- 数据完整性至关重要,因为泄露的数据可能会对用户和系统造成重大损害.
- 现有的数据管理策略难以有效地消除与入侵相关的数据.
研究的目的:
- 提出一个新的网络物理系统 (CPS) 框架,以减轻数据入侵.
- 开发一个能够区分真假数据样本和评估失败率的分析框架.
- 通过集成学习技术,尽量减少数据丢失和消除入侵.
主要方法:
- 构建一个独特的系统模型,并为CPS提供分析框架.
- 整合使用生成器和区分器的学习技术,以减少数据丢失.
- 拟议的综合框架的实时评估和模拟.
主要成果:
- 拟议的CPS框架在模拟中显著降低了故障率和数据丢失.
- 基于学习的方法实现了最小的数据损失 (1%) 和高效率 (97%).
- 模拟结果的表现优于缺乏学习组件的现有方法.
结论:
- 开发的CPS框架有效地缓解入侵,数据损失最小.
- 该框架适合实时实施,而不会影响数据完整性.
- 基于学习的方法为保护网络物理系统提供了卓越的解决方案.
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