EHFOA-ID:用于物联网入侵检测的改进的HawkFish优化驱动的混合组合
Ashraf Nadir Alswaid1, Osman Nuri Uçan1
1Altinbas University, Istanbul 34217, Turkey.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了EHFOA-ID,这是一种用于物联网 (IoT) 环境的先进入侵检测系统. 它通过优化功能选择和使用混合深层组合来实现强大的威胁识别,显著提高了准确性并减少了虚假警报.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网环境面临复杂的入侵检测挑战,由于高维数据和各种攻击向量.
- 现有的方法与阶级不平衡作斗争,并且具有高效的空间导航功能.
研究的目的:
- 提出EHFOA-ID,这是一个针对物联网的增强入侵检测框架.
- 为了应对高维度流量,异质攻击和阶级不平衡的挑战.
主要方法:
- 使用增强的HawkFish优化算法 (EHFOA) 进行联合功能选择和超参数调整.
- 采用混合深层集体学习模型来捕捉空间,时间和上下文关系.
- 集成自适应式勘探-开发,莱维飞行和多样性保护以进行优化.
主要成果:
- 在UNSW-NB15上达到>99%的准确性,在SECOM数据集上达到>98%的准确性.
- 报告的宏观F1得分高于0.97和虚假报警率低于2%.
- 在最先进的入侵检测方法中表现出优越的性能.
结论:
- EHFOA-ID为物联网入侵检测提供了一个强大而高效的解决方案.
- 统一的优化学习管道增强了概括性,减少了功能冗余性.
- 该框架有效地提高了针对各种入侵模式的决策可靠性.
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