基于量子优化器和特征金字塔网络的组合,用于云-IoT环境中的入侵检测
Rejab Hajlaoui1, Mohamed Shalaby2, Raed H C Alfilh3
1Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il City, Saudi Arabia.
Scientific reports
|February 4, 2026
概括
本研究介绍了一种改进的入侵检测系统 (IDS),使用增强功能金字塔网络 (EFPN) 和量子增强儿童绘图开发优化器 (Q-CDDO) 来实现云物联网安全. 这种新的方法在检测网络入侵方面取得了很高的准确性.
科学领域:
- 网络安全 网络安全
- 网络入侵检测系统 (IDS) 是一种网络入侵检测系统.
- 云物联网安全 云物联网安全
背景情况:
- 动态的云物联网环境需要先进的入侵检测系统 (IDS).
- 现有的解决方案在高维,异质的网络流量管理方面扎.
- 当前IDS的局限性阻碍了在复杂的网络基础设施中有效检测威胁.
研究的目的:
- 为云物联网环境提供改进的IDS结构.
- 加强高维和异质网络流量的管理.
- 为了利用量子启发的优化,实现卓越的IDS性能.
主要方法:
- 开发了一个改进的特征金字塔网络 (EFPN),适用于使用多尺度特征提取的表格网络数据.
- 集成了一个量子增强的儿童绘图开发优化器 (Q-CDDO),利用量子旋转门进行超参数调整.
- 在基准数据集上验证模型:CIC-IDS-2017和Bot-IoT.
主要成果:
- 在CIC-IDS-2017数据集上实现了96.3%的准确性.
- 在Bot-IoT数据集上实现了94.6%的准确性.
- 废除研究证实了EFPN和Q-CDDO的协同作用;可视化验证了该模型的辨别力.
结论:
- 拟议的EFPN和Q-CDDO模型显著提高了云物联网中的入侵检测能力.
- 量子启发的元启发表现出增强网络安全解决方案的巨大潜力.
- 该研究验证了集成方法对动态网络流量分析的有效性.
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