一个漂移意识的RS2FS管道与IDS的信任门
Gomathi Sakthivel1, Anitha Kumari Kumarasamy2
1Dr.N.G.P. Institute of Technology, Coimbatore, India. s.gomathi4321@gmail.com.
Scientific reports
|January 10, 2026
概括
本研究介绍了一种用于物联网 (IoT) 网络的新型入侵检测系统,利用特定阶段的特征选择和级联分类器来实现高精度和速度. 该系统有效地识别各种威胁,延迟时间最小,增强物联网安全.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 在异质物联网网络中实时入侵检测是具有挑战性的,因为设备功能多样化,动态拓和资源限制.
- 现有的系统经常在复杂的物联网环境中扎延迟,适应性和有效的功能选择.
- 轻量级的人工智能,边缘处理和自适应性安全模型对于高效的物联网网络监控至关重要.
研究的目的:
- 为异质物联网网络提出一种新,高效和准确的实时入侵检测系统 (IDS).
- 开发一个特定阶段的递归稀疏性和基于相关性的特征选择 (RS2FS) 机制.
- 实现一个可靠的支持矢量机器 (SVM) → SVM → 适应性神经模糊推理系统 (ANFIS) 级联分类器.
主要方法:
- 采用了特定阶段的RS2FS,结合了弹性网选,MI mRMR相关性,稳定性选择和边缘感知递归修剪以减少特征.
- 使用了可信度关闭的SVM→SVM→ANFIS级联分类器,将简单的案例路由到SVM,并将模两可的流量路由到ANFIS以进行可解释的决策.
- 在CICIoT2023数据集上评估了模型,使用场景持有分期 (5×分组CV) 并测量了性能指标,包括Macro-F1,Macro-AUC和延迟.
主要成果:
- 通过Macro-F1 = 0.962,Macro-AUC = 0.991和平衡精度 = 0.963在6.3ms CPU延迟和7.8MB的足迹实现了高性能.
- 与基线模型相比,显示出显著的改进,宏观F1比SVM增长了+1.9个百分点.
- 级联架构有效地管理了速度精度的权衡,在第一阶段以高精度解决了68%的窗口.
结论:
- 拟议的RS2FS和可信度级联框架代表了用于异质物联网环境的从根本上新的IDS架构.
- 该系统提供准确,校准,可解释和快速的入侵检测,适用于现实世界的物联网部署.
- 该框架显示了对噪声,动和缺失的功能的稳定性,以及有效的开放式拒绝能力.
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