通过使用人工智能驱动的整体方法,通过异常检测框架改善健康互联网的安全性.
Manal Abdullah Alohali1, Mohammad Alamgeer2, Ali M Al-Sharafi3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|September 30, 2025
概括
本研究介绍了一种人工智能驱动的方法,用于检测卫生事物互联网 (IoHT) 中的网络攻击,达到99.33%的准确性. 通过使用序列指数高尔夫优化 (EIoHTSCD-SEGO) 技术检测网络攻击来增强医疗保健物联网安全,提高了医疗保健网络安全.
科学领域:
- 网络安全 网络安全
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 由于广泛采用技术,医疗保健系统面临越来越多的网络安全威胁.
- 健康物联网 (IoHT) 设备容易受到网络攻击,需要强大的检测方法.
- 机器学习 (ML) 和人工智能 (AI) 为网络安全中的异常检测提供了先进的功能.
研究的目的:
- 开发一种人工智能驱动的技术,用于检测健康物联网 (IoHT) 环境中的网络攻击.
- 通过准确地分类标志着网络威胁的异常模式来提高IoHT的安全性.
- 提高在医疗机构中检测网络攻击的效率和准确性.
主要方法:
- 通过使用序列指数高尔夫优化 (EIoHTSCD-SEGO) 技术检测网络攻击来增强健康物联网安全性.
- 数据预处理涉及特征向量和min-max规范化的TF-IDF.
- 一组深度学习 (DL) 分类器 (RNN,BiLSTM,KELM) 使用序列指数高尔夫优化算法 (SEGOA) 进行了优化.
主要成果:
- 在网络攻击检测方面,EIoHTSCD-SEGO技术实现了99.33%的卓越准确性.
- 该方法使用基于AI的数据科学证明了异常检测的有效分类.
- 性能验证使用ECU-IoHT基准数据集进行.
结论:
- 开发的EIoHTSCD-SEGO技术显著改善了IoHT环境中的网络安全.
- 人工智能驱动的异常检测,特别是使用DL集和优化算法,对医疗保健网络安全非常有效.
- 该研究强调了先进的ML / AI技术在连接的医疗保健系统中减轻网络风险的潜力.
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