HCAP:用于保护医疗保健应用的混合网络攻击预测模型
Mohanad Faeq Ali1, Mohammed Shakir Mohmood2, Ban Salman Shukur3
1Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
这项研究引入了一种新的混合模型 (HCAP),以加强医疗物联网 (IoMT) 的网络安全. 该HCAP模型显著改善了网络攻击的预测和预防,提高了医疗保健数据安全性和IoMT系统的弹性.
科学领域:
- 网络安全 网络安全
- 医疗保健技术 技术 医疗保健 技术
- 机器学习 机器学习
背景情况:
- 医疗物联网 (IoMT) 增强了医疗保健,但引入了网络安全漏洞.
- 攻击威胁到敏感健康数据的保密性,完整性和可用性.
- 现有的模型面临着计算复杂性和过拟合问题.
研究的目的:
- 加强在IoMT环境中的网络攻击预测和预防.
- 通过机器学习提高医疗保健数据安全性和IoMT系统的弹性.
- 为了解决数据集可用性和模型效率的局限性.
主要方法:
- 开发了一种混合网络攻击预测 (HCAP) 模型.
- 使用主要组件-递归特征消除 (PC-RFE) 进行特征选择.
- 使用狮子优化技术与长短期记忆 (LSTM) 网络进行预测.
主要成果:
- 在检测网络攻击方面,HCAP模型实现了98%的准确性.
- 虚假阳性率减少了25%,虚假阴性率减少了20%.
- 与现有模型相比,计算效率提高了30%.
结论:
- HCAP模型为IoMT网络安全提供了可靠和高效的解决方案.
- 增强的威胁检测能力提高了医疗保健应用程序的整体安全性.
- 该研究强调了先进机器学习在确保关键医疗保健基础设施方面的潜力.
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