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在医疗物联网中的入侵检测系统中改进了相互信息特征选择技术

Mousa Alalhareth^1,2, Sung-Chul Hong²

¹Department of Information Systems, College of Computer Science and Information System, Najran University, Najran 61441, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

这项研究引入了一种新的功能选择方法,LRGU-MIFS,以改进医疗物联网 (IoMT) 的入侵检测系统 (IDS). 它通过准确识别用于检测医疗保健物联网环境中的威胁的关键特征来增强网络安全.

科学领域:

医疗保健中的网络安全
医疗事物的互联网 (IoMT)
机器学习用于入侵检测和检测.

背景情况:

医疗物联网 (IoMT) 能够实现远程患者监控,但也带来了重大的网络安全风险.
现有的入侵检测系统 (IDS) 难以处理高维的IoMT数据,导致过拟合和精度降低.
在IDS中当前的特征选择方法通常会因为对特征冗余性的不准确假设而失败.

研究的目的:

为IoMT入侵检测提出一种增强的特征选择技术,即物流冗余系数逐步上加权MIFS (LRGU-MIFS).
解决现有方法在处理数据稀疏性和非线性特征关系方面的局限性.
提高IDS在IoMT系统中识别网络安全威胁的准确性和效率.

主要方法:

开发了LRGU-MIFS,该系统使用后勤函数单独评估特征,以建模非线性冗余.
将LRGU指标纳入了相互信息特征选择 (MIFS) 框架.
与现有的特征选择技术对比,评估了拟议方法的性能.

主要成果:

与现有方法相比,LRGU-MIFS确定了一组更紧和更重要的特征.
拟议的技术有效地处理数据稀疏性和非线性特征相互依赖性.
实验结果表明,在IoMT网络安全的功能选择中,性能优越.

结论:

LRGU-MIFS为IoMT入侵检测中的功能选择提供了一个强大的解决方案.
该方法通过准确识别突出特征,提高了IDS检测威胁的能力.
这一进步有助于在联网医疗环境中保护患者数据和福祉.

关键词:

在IDS IDS中,您可以使用一个小小的东西.在 LRGU 和 LRGU 之间.在MIFS中,MIFS是MIFS,MIFS是MIFS.深度学习是一种深度学习.功能选择功能选择机器学习是机器学习.

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