基于智能注意力的深层复杂学习 (IADCL) 模型,用于智能医疗保健安全
J Maruthupandi1, S Sivakumar2, B Lakshmi Dhevi3
1Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India.
Scientific reports
|January 8, 2025
概括
本研究介绍了一种改进的入侵检测系统,用于智能医疗保健物联网 (IoT) 设备. 开发的框架有效地检测网络威胁,增强连接卫生系统的安全性.
科学领域:
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 物联网在医疗保健系统等智能基础设施中的快速扩张.
- 现有的安全措施对于资源有限的物联网设备来说,不足以应对不断变化的网络威胁.
研究的目的:
- 为智能医疗物联网系统提出一个增强的安全架构,入侵攻击检测和分类 (IADCL).
- 利用公开可用的数据集开发一个强大的网络威胁检测框架.
主要方法:
- 使用CIC-IDS 2017,CIC-IDS 2018,CIC-Bell DNS 2021和NSL-KDD数据集进行框架开发.
- 实施了入侵减少和知识优化 (IRKO) 方法来进行特征选择.
- 使用自适应卷积贝叶斯网络 (AConBN) 分类器进行流量分类.
- 使用Sine-Annealing-Harris Hawks优化 (SA-HHO) 算法进行优化分类.
主要成果:
- IADCL框架在检测网络攻击方面表现出高度准确性.
- 绩效评估证实了该系统在关键指标上的有效性.
- 通过IRKO的特征减少,提高了检测过程的效率.
结论:
- 拟议的IADCL框架为保护智能医疗物联网设备提供了一个强大的解决方案.
- 该系统显示出在连接的医疗环境中减轻网络威胁的巨大潜力.
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