在医疗保健系统中加强威胁检测,随机合启动组合分类器
Mohammad Alhefdi1, Yosuef Alotaibi1, Paul Rodrigues1
1Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Saudi Arabia.
Health informatics journal
|March 2, 2026
概括
一个新的随机合启动组合分类器 (RCBEC) 通过改进入侵检测来增强智能医疗保健中的网络安全. 这种机器学习模型显著提高了患者数据保护和对物联网支持系统的信任.
科学领域:
- 网络安全 网络安全
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 由于物联网 (IoT) 技术的整合,现代医疗保健面临着严重的患者数据保护挑战.
- 现有的网络安全措施很难有效地检测互联的医疗保健环境中复杂的网络攻击.
- 确保患者保密性和系统完整性在不断变化的数字健康环境中至关重要.
研究的目的:
- 为智能医疗环境引入先进的入侵检测框架,即随机合启动组合分类器 (RCBEC).
- 优化RCBEC模型,以提高识别网络威胁的准确性,计算效率和精度.
- 加强支持物联网的医疗保健系统的安全性,并保护敏感的患者信息.
主要方法:
- 用于数据预处理的十进制得分最大规范化,包括转换,重复删除和缺失值的归算.
- 使用K-Best内核区分分析 (K-BKDA) 进行特征提取,以及猎犬算法 (HCA) 进行特征选择和优化.
- 在基于Python的ECU-IoHT环境中实现RCBEC模型,用于入侵检测.
主要成果:
- 该RCBEC模型实现了高精度和99.6%的F1得分,超过了现有的入侵检测方法.
- 演示了快速的计算性能与强大的威胁识别能力相结合.
- 在比较分析中,在各种数据集中展示了卓越的概括性和适应性.
结论:
- 该RCBEC模型提供了一种弹性和智能机制,用于检测和减轻医疗保健网络中的网络安全威胁.
- 机器学习驱动的入侵检测显著加强了患者数据保护,操作可靠性和对下一代医疗保健系统的信任.
- 拟议的框架在保护物联网支持的医疗保健基础设施免受网络攻击方面取得了重大进展.
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