An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets

The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging. As the IIoT becomes integral to critical infrastructure, e...

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Vydané v:	Future internet Ročník 17; číslo 7; s. 279
Hlavní autori:	Salehiyan, Ahmad, Moghaddam, Pardis Sadatian, Kaveh, Masoud
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 24.06.2025
Predmet:	Accuracy Artificial intelligence Artificial neural networks autoencoder Belief networks chimp optimization algorithm Complexity Critical infrastructure Cybersecurity Datasets Deep learning Edge computing Efficiency Feature extraction Feature selection generative adversarial network Generative adversarial networks Industrial applications Industrial Internet of Things intrusion detection Intrusion detection systems Machine learning Optimization Pattern recognition Recall Robustness (mathematics) Run time (computers) Spectrum allocation Threats transformer
ISSN:	1999-5903, 1999-5903
On-line prístup:	Získať plný text
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