On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples

Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which...

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Vydané v:	Future generation computer systems Ročník 133; s. 213 - 227
Hlavní autori:	Zhang, Ying, Liu, Qiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.08.2022
Predmet:	Borderline-SMOTE Conditional Variational Autoencoder Data augmentation IoT intrusion detection IoT intrusion detection Data augmentation Borderline-SMOTE Conditional Variational Autoencoder
ISSN:	0167-739X, 1872-7115
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