TwinGuard: A Supervised Machine Learning Framework for DoS Attack Detection in IoT-Enabled Digital Twins Using Random Forest and Feature Selection Optimization
Digital Twin (DT) technology enables real-time monitoring of Internet of Things (IoT)-integrated systems but faces severe threats from Denial-of-Service (DoS) attacks, which compromise data accuracy and operational stability. Existing security solutions struggle to adapt to these threats, creating a...
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| Huvudupphov: | , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
IEEE
2025-01-01
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| Serie: | IEEE Access |
| Ämnen: | |
| Länkar: | https://ieeexplore.ieee.org/document/11226999/ |
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