XAI-XGBoost:一种创新的可解释入侵检测方法,用于保护医疗物联网系统的安全
Yousif Hosain1, Muhammet Çakmak2
1Department of Computer Engineering, Karabuk University, Karabuk, 78050, Turkey. 2138166007@ogrenci.karabuk.edu.tr.
Scientific reports
|July 2, 2025
概括
一个新的可解释的入侵检测系统 (IDS) 增强了医疗物联网 (IoMT) 的网络安全. 这种由人工智能驱动的框架确保了高准确性和透明度,保护了患者数据和医疗保健网络的安全.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 医疗物联网 (IoMT) 正在彻底改变医疗保健,但也面临着重大的网络安全威胁.
- 现有的入侵检测系统 (IDS) 在数据不平衡,可解释性和现实世界医疗保健部署方面扎.
- 在IoMT环境中,患者安全和数据完整性至关重要.
研究的目的:
- 为 IoMT 系统开发一个创新的和可解释的 IDS 框架.
- 确保医疗保健网络安全的高检测准确度和模型透明度.
- 在敏感的医疗环境中解决传统IDS的局限性.
主要方法:
- 混合随机抽样,以解决阶级不平衡问题.
- 递归特征消除 (RFE) 用于优化特征选择.
- 一个优化的XGBoost分类器与SHAP和LIME集成,用于解释AI.
主要成果:
- 在WUSTL-EHMS-2020数据集上实现了99.22%的准确性,98.35%的精度,99.91%的回忆,99.12%的F1得分和100%的ROC-AUC.
- 与传统的ML模型和最先进的IDS相比,表现出卓越的性能.
- 为模型预测提供全球和本地洞察力,提高可信度.
结论:
- 可解释性是医疗保健网络安全的基本要求,而不是可选功能.
- 拟议的框架为IoMT系统提供了强大,可解释和安全的解决方案.
- 这项工作促进了人工智能驱动的安全,以实现更安全,更负责任的IoMT生态系统.
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