深入研究人工智能,并增强基于优化的安全漏洞检测,在健康互联网上实现了智能城市环境
S Jayanthi1, Sodagudi Suhasini2, N Sharmili3
1Department of Artificial Intelligence & Data Science, Faculty of Science and Technology (IcfaiTech), The ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana, 01 503, India.
Scientific reports
|July 2, 2025
概括
这项研究介绍了一种新的网络攻击检测模型,SADBN-AMOA,用于智能城市的健康物联网 (IoHT). 该模型实现了98.71%的准确性,提高了连接医疗保健设备的安全性.
科学领域:
- 医疗保健中的网络安全
- 卫生事物的互联网 (IoHT)
- 智慧城市技术 智慧城市技术
背景情况:
- 对于医疗保健来说,IoHT设备越来越重要,但它们容易受到网络攻击.
- 有效的入侵检测系统 (IDS) 对智能城市的IoHT安全至关重要.
- 深度学习 (DL) 为识别已知和未知的网络威胁提供了先进的能力.
研究的目的:
- 为智能城市IoHT环境开发强大的网络攻击检测方法.
- 减轻安全风险,提高IoHT医疗保健系统的弹性.
- 提高IoHT网络入侵检测的准确性和效率.
主要方法:
- 通过深度信念网络和先进的元启发优化算法 (SADDBN-AMOA) 模型提出了安全攻击检测的建议.
- 使用Z-score规范化进行数据预处理.
- 使用粘泥模具优化 (SMO) 进行特征选择和深度信念网络 (DBN) 进行分类.
- 使用改进的哈里斯霍克优化 (IHHO) 算法微调的DBN超参数.
主要成果:
- SADDBN-AMOA模型在物联网医疗保健安全数据集上显示了98.71%的高精度.
- 与现有的网络攻击检测模型相比,实现了卓越的性能.
- 验证了该模型在识别和减轻IoHT网络中的安全威胁方面的有效性.
结论:
- SADDBN-AMOA模型为智能城市IoHT环境中的网络攻击检测提供了高度有效和准确的解决方案.
- 整合DL和metaheuristic优化显著提高了入侵检测能力.
- 拟议的方法对于保护敏感的医疗数据和确保IoHT服务的可靠性至关重要.
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