数据集用于医疗保健领域的分布式拒绝服务检测,物联网环境
Mirza Akhi1, Ciarán Eising1, Lubna Luxmi Dhirani1
1Department of Electronic and Computer Engineering, University of Limerick, Limerick, V94 T9PX, Ireland.
Data in brief
|January 1, 2026
概括
医疗保健物联网 (H-IoT) 安全地址分布式拒绝服务 (DDoS) 攻击的新数据集. 这些标记的数据集使强大的机器学习模型能够在连接的医疗环境中增强网络安全.
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:
- 在医疗机构中物联网 (IoT) 设备的扩散带来了重大安全挑战,特别是针对可以破坏关键服务的分布式拒绝服务 (DDoS) 攻击.
- 现有的数据集往往缺乏在医疗保健物联网 (H-IoT) 环境中进行有效安全分析所需的特定流量属性.
研究的目的:
- 引入两个新的标记数据集,UL-ECE-MQTT-DDoS-H-IoT2025和UL-ECE-UDP-DDoS-H-IoT2025,旨在促进H-IoT安全方面的研究.
- 为H-IoT网络提供现实的流量模拟,包括正常操作和DDoS攻击场景.
主要方法:
- 使用Cooja和ns-3模拟器生成两个数据集,在正常和DDoS条件下模拟现实的H-IoT流量模式.
- MQTT数据集捕获了医疗保健特定的传感器数据和网络元数据; UDP数据集包括支持5G的H-IoT网络跟踪以及详细的数据包信息.
- 处理原始模拟日志进入结构化的CSV文件,其中包含诸如时间,有效载荷大小,消息频率和节点级统计数据等功能.
主要成果:
- 这些数据集包含了H-IoT特定的通信参数,例如节点级消息计数和监控频率,比通用数据集提供了更现实的表示.
- 这些数据集能够进行详细的分析来检测DDoS威胁,包括UDP洪水变体,并适合开发和比较机器学习 (ML) 和深度学习 (DL) 模型.
- MQTT数据集包含来自5小时模拟的约20,080条记录,而UDP数据集包含来自120秒模拟的约99,887条记录.
结论:
- UL-ECE-MQTT-DDoS-H-IoT2025和UL-ECE-UDP-DDoS-H-IoT2025数据集为推进H-IoT生态系统中的网络安全提供了宝贵的资源.
- 这些数据集支持基于ML/DL的先进机制的开发,用于在医疗环境中强有力的检测复杂的网络威胁.
- 创建的数据集为未来研究物联网领域中新兴攻击场景和安全解决方案奠定了基础.
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