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虚假数据注入攻击数据集用于工业5.0中的IIoT的分类,识别和检测
A K M Ahasan Habib1, Mohammad Kamrul Hasan1, Rosilah Hassan1
1Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor 43600, Malaysia.
Data in brief
|June 16, 2025
概括
一个新的数据集,UKMNCT_IIoT_FDIA,有助于检测工业物联网 (IIoT) 系统中的虚假数据注入 (FDI) 攻击,用于工业5.0. 该资源使更好的机器学习模型能够保护IIoT环境免受网络威胁.
科学领域:
- 网络安全 网络安全
- 事物的工业互联网 (IIoT)
- 工业5.0 工业 5.0 工业 5.0 工业 5.0 工业
背景情况:
- 工业物联网 (IIoT) 设备在工业5.0中的扩散引入了重要的安全漏洞.
- 虚假数据注入 (FDI) 攻击构成了关键威胁,危及连接的IIoT设备的可用性和运行.
研究的目的:
- 引入UKMNCT_IIoT_FDIA数据集,用于分类,识别和检测工业5.0 IIoT生态系统中的外国直接投资攻击.
- 提供全面和独立的数据集,准确地描述各种物联网环境和网络配置.
主要方法:
- 开发和方法检查UKMNCT_IIoT_FDIA数据集,评估其特征与现实世界IIoT系统相比.
- 包含各种FDI攻击场景,包括不同的方法和强度,以模拟动态威胁景观.
- 使用数据集开发和评估机器学习 (ML) 和深度学习 (DL) 算法来检测外国直接投资的攻击.
主要成果:
- UKMNCT_IIoT_FDIA数据集有效地代表了IIoT环境中的动态外国直接投资攻击威胁.
- 该数据集有助于创建和评估强大的ML和DL算法,以有效地检测外国直接投资的攻击.
- 积极攻击检测,恶意设备识别和攻击分类的证明方法显示出显著的有效性.
结论:
- 英国MNCT_IIoT_FDIA数据集是促进工业5.0 IIoT网络安全的宝贵资源.
- 使用数据集的拟议方法显示了提高IIoT环境的安全性和安全性的潜力.
- 有效地检测,识别和分类FDI攻击对于保护工业5.0生态系统至关重要.
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