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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Hlavní autoři:	Norah Alhamam, M. M. Hafizur Rahman, Ahmed Aljughaiman
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Digital twin IoT security DoS attack detection feature selection Industry 4.0
On-line přístup:	https://ieeexplore.ieee.org/document/11226999/
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